diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyONNXModelWithInMemoryImages.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyONNXModelWithInMemoryImages.cs
index 430ad8fbfc..4c1c3a3d4b 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyONNXModelWithInMemoryImages.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyONNXModelWithInMemoryImages.cs
@@ -15,44 +15,61 @@ public static void Example()
             // Download the squeeznet image model from ONNX model zoo, version 1.2
             // https://github.com/onnx/models/tree/master/squeezenet or use
             // Microsoft.ML.Onnx.TestModels nuget.
-            // It's a multiclass classifier. It consumes an input "data_0" and produces
-            // an output "softmaxout_1".
+            // It's a multiclass classifier. It consumes an input "data_0" and
+            // produces an output "softmaxout_1".
             var modelPath = @"squeezenet\00000001\model.onnx";
 
             // Create ML pipeline to score the data using OnnxScoringEstimator
             var mlContext = new MLContext();
 
-            // Create in-memory data points. Its Image/Scores field is the input/output of the used ONNX model.
+            // Create in-memory data points. Its Image/Scores field is the
+            // input /output of the used ONNX model.
             var dataPoints = new ImageDataPoint[]
             {
                 new ImageDataPoint(Color.Red),
                 new ImageDataPoint(Color.Green)
             };
 
-            // Convert training data to IDataView, the general data type used in ML.NET.
+            // Convert training data to IDataView, the general data type used in
+            // ML.NET.
             var dataView = mlContext.Data.LoadFromEnumerable(dataPoints);
 
-            // Create a ML.NET pipeline which contains two steps. First, ExtractPixle is used to convert the 224x224 image to a 3x224x224 float tensor.
-            // Then the float tensor is fed into a ONNX model with an input called "data_0" and an output called "softmaxout_1". Note that "data_0" and
-            // "softmaxout_1" are model input and output names stored in the used ONNX model file. Users may need to inspect their own models to
-            // get the right input and output column names.
-            var pipeline = mlContext.Transforms.ExtractPixels("data_0", "Image")                   // Map column "Image" to column "data_0"
-                .Append(mlContext.Transforms.ApplyOnnxModel("softmaxout_1", "data_0", modelPath)); // Map column "data_0" to column "softmaxout_1"
+            // Create a ML.NET pipeline which contains two steps. First,
+            // ExtractPixle is used to convert the 224x224 image to a 3x224x224
+            // float tensor. Then the float tensor is fed into a ONNX model with an
+            // input called "data_0" and an output called "softmaxout_1". Note that
+            // "data_0" and "softmaxout_1" are model input and output names stored
+            // in the used ONNX model file. Users may need to inspect their own
+            // models to get the right input and output column names.
+            // Map column "Image" to column "data_0"
+            // Map column "data_0" to column "softmaxout_1"
+            var pipeline = mlContext.Transforms.ExtractPixels("data_0", "Image")
+                .Append(mlContext.Transforms.ApplyOnnxModel("softmaxout_1",
+                "data_0", modelPath)); 
+
             var model = pipeline.Fit(dataView);
             var onnx = model.Transform(dataView);
 
-            // Convert IDataView back to IEnumerable<ImageDataPoint> so that user can inspect the output, column "softmaxout_1", of the ONNX transform.
-            // Note that Column "softmaxout_1" would be stored in ImageDataPont.Scores because the added attributed [ColumnName("softmaxout_1")]
-            // tells that ImageDataPont.Scores is equivalent to column "softmaxout_1".
-            var transformedDataPoints = mlContext.Data.CreateEnumerable<ImageDataPoint>(onnx, false).ToList();
+            // Convert IDataView back to IEnumerable<ImageDataPoint> so that user
+            // can inspect the output, column "softmaxout_1", of the ONNX transform.
+            // Note that Column "softmaxout_1" would be stored in ImageDataPont
+            //.Scores because the added attributed [ColumnName("softmaxout_1")]
+            // tells that ImageDataPont.Scores is equivalent to column
+            // "softmaxout_1".
+            var transformedDataPoints = mlContext.Data.CreateEnumerable<
+                ImageDataPoint>(onnx, false).ToList();
 
-            // The scores are probabilities of all possible classes, so they should all be positive. 
+            // The scores are probabilities of all possible classes, so they should
+            // all be positive. 
             foreach (var dataPoint in transformedDataPoints)
             {
                 var firstClassProb = dataPoint.Scores.First();
                 var lastClassProb = dataPoint.Scores.Last();
-                Console.WriteLine($"The probability of being the first class is {firstClassProb * 100}%.");
-                Console.WriteLine($"The probability of being the last class is {lastClassProb * 100}%.");
+                Console.WriteLine("The probability of being the first class is " + 
+                    (firstClassProb * 100) + "%.");
+
+                Console.WriteLine($"The probability of being the last class is " +
+                    (lastClassProb * 100) + "%.");
             }
 
             // Expected output:
@@ -62,7 +79,8 @@ public static void Example()
             //  The probability of being the last class is 0.394428%.
         }
 
-        // This class is used in Example() to describe data points which will be consumed by ML.NET pipeline.
+        // This class is used in Example() to describe data points which will be
+        // consumed by ML.NET pipeline.
         private class ImageDataPoint
         {
             // Height of Image.
@@ -75,9 +93,9 @@ private class ImageDataPoint
             [ImageType(height, width)]
             public Bitmap Image { get; set; }
 
-            // Expected output of ONNX model. It contains probabilities of all classes.
-            // Note that the ColumnName below should match the output name in the used
-            // ONNX model file.
+            // Expected output of ONNX model. It contains probabilities of all
+            // classes. Note that the ColumnName below should match the output name
+            // in the used ONNX model file.
             [ColumnName("softmaxout_1")]
             public float[] Scores { get; set; }
 
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyOnnxModel.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyOnnxModel.cs
index abff3a339d..adb2268060 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyOnnxModel.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyOnnxModel.cs
@@ -19,14 +19,16 @@ public static void Example()
 
             // Generate sample test data.
             var samples = GetTensorData();
-            // Convert training data to IDataView, the general data type used in ML.NET.
+            // Convert training data to IDataView, the general data type used in
+            // ML.NET.
             var data = mlContext.Data.LoadFromEnumerable(samples);
             // Create the pipeline to score using provided onnx model.
             var pipeline = mlContext.Transforms.ApplyOnnxModel(modelPath);
             // Fit the pipeline and get the transformed values
             var transformedValues = pipeline.Fit(data).Transform(data);
             // Retrieve model scores into Prediction class
-            var predictions = mlContext.Data.CreateEnumerable<Prediction>(transformedValues, reuseRowObject: false);
+            var predictions = mlContext.Data.CreateEnumerable<Prediction>(
+                transformedValues, reuseRowObject: false);
 
             // Iterate rows
             foreach (var prediction in predictions)
@@ -34,7 +36,8 @@ public static void Example()
                 int numClasses = 0;
                 foreach (var classScore in prediction.softmaxout_1.Take(3))
                 {
-                    Console.WriteLine($"Class #{numClasses++} score = {classScore}");
+                    Console.WriteLine("Class #" + numClasses++ + " score = " +
+                        classScore);
                 }
                 Console.WriteLine(new string('-', 10));
             }
@@ -65,9 +68,14 @@ public class TensorData
         public static TensorData[] GetTensorData()
         {
             // This can be any numerical data. Assume image pixel values.
-            var image1 = Enumerable.Range(0, inputSize).Select(x => (float)x / inputSize).ToArray();
-            var image2 = Enumerable.Range(0, inputSize).Select(x => (float)(x + 10000) / inputSize).ToArray();
-            return new TensorData[] { new TensorData() { data_0 = image1 }, new TensorData() { data_0 = image2 } };
+            var image1 = Enumerable.Range(0, inputSize).Select(x => (float)x /
+                inputSize).ToArray();
+
+            var image2 = Enumerable.Range(0, inputSize).Select(x => (float)(x +
+                10000) / inputSize).ToArray();
+
+            return new TensorData[] { new TensorData() { data_0 = image1 }, new
+                TensorData() { data_0 = image2 } };
         }
 
         // Class to contain the output values from the transformation.
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApproximatedKernelMap.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApproximatedKernelMap.cs
index ce7152b50f..7b21512883 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApproximatedKernelMap.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApproximatedKernelMap.cs
@@ -9,11 +9,12 @@ namespace Samples.Dynamic
 {
     public static class ApproximatedKernelMap
     {
-        // Transform feature vector to another non-linear space. See https://people.eecs.berkeley.edu/~brecht/papers/07.rah.rec.nips.pdf.
+        // Transform feature vector to another non-linear space. See
+        // https://people.eecs.berkeley.edu/~brecht/papers/07.rah.rec.nips.pdf.
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
             var samples = new List<DataPoint>()
             {
@@ -22,19 +23,26 @@ public static void Example()
                 new DataPoint(){ Features = new float[7] {-1, 1, 0,-1,-1, 0,-1} },
                 new DataPoint(){ Features = new float[7] { 0,-1, 0, 1, 0,-1,-1} }
             };
-            // Convert training data to IDataView, the general data type used in ML.NET.
+            // Convert training data to IDataView, the general data type used in
+            // ML.NET.
             var data = mlContext.Data.LoadFromEnumerable(samples);
-            // ApproximatedKernel map takes data and maps it's to a random low-dimensional space.
-            var approximation = mlContext.Transforms.ApproximatedKernelMap("Features", rank: 4, generator: new GaussianKernel(gamma: 0.7f), seed: 1);
+            // ApproximatedKernel map takes data and maps it's to a random
+            // low -dimensional space.
+            var approximation = mlContext.Transforms.ApproximatedKernelMap(
+                "Features", rank: 4, generator: new GaussianKernel(gamma: 0.7f),
+                seed: 1);
 
-            // Now we can transform the data and look at the output to confirm the behavior of the estimator.
-            // This operation doesn't actually evaluate data until we read the data below.
+            // Now we can transform the data and look at the output to confirm the
+            // behavior of the estimator. This operation doesn't actually evaluate
+            // data until we read the data below.
             var tansformer = approximation.Fit(data);
             var transformedData = tansformer.Transform(data);
 
             var column = transformedData.GetColumn<float[]>("Features").ToArray();
             foreach (var row in column)
-                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString("f4"))));
+                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString(
+                    "f4"))));
+
             // Expected output:
             // -0.0119, 0.5867, 0.4942,  0.7041
             //  0.4720, 0.5639, 0.4346,  0.2671
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CalculateFeatureContribution.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CalculateFeatureContribution.cs
index 3a342f8809..2f0f5b464e 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CalculateFeatureContribution.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CalculateFeatureContribution.cs
@@ -9,7 +9,8 @@ public static class CalculateFeatureContribution
     {
         public static void Example()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, 
             // as a catalog of available operations and as the source of randomness.
             var mlContext = new MLContext(seed: 1);
 
@@ -19,7 +20,8 @@ public static void Example()
             // Convert training data to IDataView.
             var data = mlContext.Data.LoadFromEnumerable(samples);
 
-            // Create a pipeline to concatenate the features into a feature vector and normalize it.
+            // Create a pipeline to concatenate the features into a feature vector
+            // and normalize it.
             var transformPipeline = mlContext.Transforms.Concatenate("Features",
                     new string[] { nameof(Data.Feature1), nameof(Data.Feature2) })
                 .Append(mlContext.Transforms.NormalizeMeanVariance("Features"));
@@ -37,28 +39,45 @@ public static void Example()
             var linearModel = linearTrainer.Fit(transformedData);
             // Print the model parameters.
             Console.WriteLine($"Linear Model Parameters");
-            Console.WriteLine($"Bias: {linearModel.Model.Bias} Feature1: {linearModel.Model.Weights[0]} Feature2: {linearModel.Model.Weights[1]}");
+            Console.WriteLine("Bias: " + linearModel.Model.Bias+ " Feature1: " +
+                linearModel.Model.Weights[0] + " Feature2: " +linearModel.Model
+                .Weights[1]);
 
-            // Define a feature contribution calculator for all the features, and don't normalize the contributions.
-            // These are "trivial estimators" and they don't need to fit to the data, so we can feed a subset.
-            var simpleScoredDataset = linearModel.Transform(mlContext.Data.TakeRows(transformedData, 1));
-            var linearFeatureContributionCalculator = mlContext.Transforms.CalculateFeatureContribution(linearModel, normalize: false).Fit(simpleScoredDataset);
+            // Define a feature contribution calculator for all the features, and
+            // don't normalize the contributions.These are "trivial estimators" and
+            // they don't need to fit to the data, so we can feed a subset.
+            var simpleScoredDataset = linearModel.Transform(mlContext.Data
+                .TakeRows(transformedData, 1));
+
+            var linearFeatureContributionCalculator = mlContext.Transforms
+                .CalculateFeatureContribution(linearModel, normalize: false).Fit(
+                simpleScoredDataset);
 
             // Create a transformer chain to describe the entire pipeline.
-            var scoringPipeline = transformer.Append(linearModel).Append(linearFeatureContributionCalculator);
+            var scoringPipeline = transformer.Append(linearModel).Append(
+                linearFeatureContributionCalculator);
 
-            // Create the prediction engine to get the features extracted from the text.
-            var predictionEngine = mlContext.Model.CreatePredictionEngine<Data, ScoredData>(scoringPipeline);
+            // Create the prediction engine to get the features extracted from the
+            // text.
+            var predictionEngine = mlContext.Model.CreatePredictionEngine<Data,
+                ScoredData>(scoringPipeline);
 
             // Convert the text into numeric features.
             var prediction = predictionEngine.Predict(samples.First());
 
             // Write out the prediction, with contributions.
-            // Note that for the linear model, the feature contributions for a feature in an example is the feature-weight*feature-value.
+            // Note that for the linear model, the feature contributions for a
+            // feature in an example is the feature-weight*feature-value.
             // The total prediction is thus the bias plus the feature contributions.
-            Console.WriteLine($"Label: {prediction.Label} Prediction: {prediction.Score}");
-            Console.WriteLine($"Feature1: {prediction.Features[0]} Feature2: {prediction.Features[1]}");
-            Console.WriteLine($"Feature Contributions: {prediction.FeatureContributions[0]} {prediction.FeatureContributions[1]}");
+            Console.WriteLine("Label: " + prediction.Label + " Prediction: " +
+                prediction.Score);
+
+            Console.WriteLine("Feature1: " + prediction.Features[0] + 
+                " Feature2: " + prediction.Features[1]);
+
+            Console.WriteLine("Feature Contributions: " + prediction
+                .FeatureContributions[0] + " " + prediction
+                .FeatureContributions[1]);
 
             // Expected output:
             //  Linear Model Parameters
@@ -107,7 +126,9 @@ private static IEnumerable<Data> GenerateData(int nExamples = 10000,
                 };
 
                 // Create a noisy label.
-                data.Label = (float)(bias + weight1 * data.Feature1 + weight2 * data.Feature2 + rng.NextDouble() - 0.5);
+                data.Label = (float)(bias + weight1 * data.Feature1 + weight2 *
+                    data.Feature2 + rng.NextDouble() - 0.5);
+
                 yield return data;
             }
         }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CalculateFeatureContributionCalibrated.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CalculateFeatureContributionCalibrated.cs
index c05e7e5468..adaf66389c 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CalculateFeatureContributionCalibrated.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CalculateFeatureContributionCalibrated.cs
@@ -9,8 +9,9 @@ public static class CalculateFeatureContributionCalibrated
     {
         public static void Example()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
-            // as a catalog of available operations and as the source of randomness.
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness.
             var mlContext = new MLContext();
 
             // Create a small dataset.
@@ -19,7 +20,8 @@ public static void Example()
             // Convert training data to IDataView.
             var data = mlContext.Data.LoadFromEnumerable(samples);
 
-            // Create a pipeline to concatenate the features into a feature vector and normalize it.
+            // Create a pipeline to concatenate the features into a feature vector
+            // and normalize it.
             var transformPipeline = mlContext.Transforms.Concatenate("Features", 
                     new string[] { nameof(Data.Feature1), nameof(Data.Feature2) })
                 .Append(mlContext.Transforms.NormalizeMeanVariance("Features"));
@@ -31,7 +33,8 @@ public static void Example()
             var transformedData = transformer.Transform(data);
 
             // Define a linear trainer.
-            var linearTrainer = mlContext.BinaryClassification.Trainers.SdcaLogisticRegression();
+            var linearTrainer = mlContext.BinaryClassification.Trainers
+                .SdcaLogisticRegression();
 
             // Now we train the model and score it on the transformed data.
             var linearModel = linearTrainer.Fit(transformedData);
@@ -42,26 +45,42 @@ public static void Example()
                 linearModel.Model.SubModel.Weights[0],
                 linearModel.Model.SubModel.Weights[1]);
 
-            // Define a feature contribution calculator for all the features, and don't normalize the contributions.
-            // These are "trivial estimators" and they don't need to fit to the data, so we can feed a subset.
-            var simpleScoredDataset = linearModel.Transform(mlContext.Data.TakeRows(transformedData, 1));
-            var linearFeatureContributionCalculator = mlContext.Transforms.CalculateFeatureContribution(linearModel, normalize: false).Fit(simpleScoredDataset);
+            // Define a feature contribution calculator for all the features, and
+            // don't normalize the contributions. These are "trivial estimators" and
+            // they don't need to fit to the data, so we can feed a subset.
+            var simpleScoredDataset = linearModel.Transform(mlContext.Data
+                .TakeRows(transformedData, 1));
+
+            var linearFeatureContributionCalculator = mlContext.Transforms
+                .CalculateFeatureContribution(linearModel, normalize: false)
+                .Fit(simpleScoredDataset);
 
             // Create a transformer chain to describe the entire pipeline.
-            var scoringPipeline = transformer.Append(linearModel).Append(linearFeatureContributionCalculator);
+            var scoringPipeline = transformer.Append(linearModel)
+                .Append(linearFeatureContributionCalculator);
 
-            // Create the prediction engine to get the features extracted from the text.
-            var predictionEngine = mlContext.Model.CreatePredictionEngine<Data, ScoredData>(scoringPipeline);
+            // Create the prediction engine to get the features extracted from the
+            // text.
+            var predictionEngine = mlContext.Model.CreatePredictionEngine<Data,
+                ScoredData>(scoringPipeline);
 
             // Convert the text into numeric features.
             var prediction = predictionEngine.Predict(samples.First());
 
             // Write out the prediction, with contributions.
-            // Note that for the linear model, the feature contributions for a feature in an example is the feature-weight*feature-value.
-            // The total prediction is thus the bias plus the feature contributions.
-            Console.WriteLine($"Label: {prediction.Label} Prediction-Score: {prediction.Score} Prediction-Probability: {prediction.Probability}");
-            Console.WriteLine($"Feature1: {prediction.Features[0]} Feature2: {prediction.Features[1]}");
-            Console.WriteLine($"Feature Contributions: {prediction.FeatureContributions[0]} {prediction.FeatureContributions[1]}");
+            // Note that for the linear model, the feature contributions for a
+            // feature in an example is the feature-weight*feature-value. The total
+            // prediction is thus the bias plus the feature contributions.
+            Console.WriteLine("Label: " + prediction.Label + " Prediction-Score: " +
+                prediction.Score + " Prediction-Probability: " + prediction
+                .Probability);
+
+            Console.WriteLine("Feature1: " + prediction.Features[0] + " Feature2: "
+                + prediction.Features[1]);
+
+            Console.WriteLine("Feature Contributions: " + prediction
+                .FeatureContributions[0] + " " + prediction
+                .FeatureContributions[1]);
 
             // Expected output:
             //  Linear Model Parameters
@@ -114,7 +133,9 @@ private static IEnumerable<Data> GenerateData(int nExamples = 10000,
                 };
 
                 // Create a Boolean label with noise.
-                var value = bias + weight1 * data.Feature1 + weight2 * data.Feature2 + rng.NextDouble() - 0.5;
+                var value = bias + weight1 * data.Feature1 + weight2 * data.Feature2
+                    + rng.NextDouble() - 0.5;
+
                 data.Label = Sigmoid(value) > 0.5;
                 yield return data;
             }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Concatenate.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Concatenate.cs
index 9b350227be..c62cc6b743 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Concatenate.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Concatenate.cs
@@ -9,38 +9,56 @@ public static class Concatenate
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
             // Create a small dataset as an IEnumerable.
             var samples = new List<InputData>()
             {
-                new InputData(){ Feature1 = 0.1f, Feature2 = new[]{ 1.1f, 2.1f, 3.1f}, Feature3 = 1 },
-                new InputData(){ Feature1 = 0.2f, Feature2 = new[]{ 1.2f, 2.2f, 3.2f}, Feature3 = 2 },
-                new InputData(){ Feature1 = 0.3f, Feature2 = new[]{ 1.3f, 2.3f, 3.3f}, Feature3 = 3 },
-                new InputData(){ Feature1 = 0.4f, Feature2 = new[]{ 1.4f, 2.4f, 3.4f}, Feature3 = 4 },
-                new InputData(){ Feature1 = 0.5f, Feature2 = new[]{ 1.5f, 2.5f, 3.5f}, Feature3 = 5 },
-                new InputData(){ Feature1 = 0.6f, Feature2 = new[]{ 1.6f, 2.6f, 3.6f}, Feature3 = 6 },
+                new InputData(){ Feature1 = 0.1f, Feature2 = new[]{ 1.1f, 2.1f,
+                    3.1f }, Feature3 = 1 },
+
+                new InputData(){ Feature1 = 0.2f, Feature2 = new[]{ 1.2f, 2.2f,
+                    3.2f }, Feature3 = 2 },
+
+                new InputData(){ Feature1 = 0.3f, Feature2 = new[]{ 1.3f, 2.3f,
+                    3.3f }, Feature3 = 3 },
+
+                new InputData(){ Feature1 = 0.4f, Feature2 = new[]{ 1.4f, 2.4f,
+                    3.4f }, Feature3 = 4 },
+
+                new InputData(){ Feature1 = 0.5f, Feature2 = new[]{ 1.5f, 2.5f,
+                    3.5f }, Feature3 = 5 },
+
+                new InputData(){ Feature1 = 0.6f, Feature2 = new[]{ 1.6f, 2.6f,
+                    3.6f }, Feature3 = 6 },
             };
 
             // Convert training data to IDataView.
             var dataview = mlContext.Data.LoadFromEnumerable(samples);
 
-            // A pipeline for concatenating the "Feature1", "Feature2" and "Feature3" columns together into a vector that will be the Features column.
-            // Concatenation is necessary because trainers take feature vectors as inputs.
+            // A pipeline for concatenating the "Feature1", "Feature2" and
+            // "Feature3" columns together into a vector that will be the Features
+            // column. Concatenation is necessary because trainers take feature
+            // vectors as inputs.
             //
-            // Please note that the "Feature3" column is converted from int32 to float using the ConvertType.
-            // The Concatenate requires all columns to be of same type.
-            var pipeline = mlContext.Transforms.Conversion.ConvertType("Feature3", outputKind: DataKind.Single)
-                .Append(mlContext.Transforms.Concatenate("Features", new[] { "Feature1", "Feature2", "Feature3" }));
+            // Please note that the "Feature3" column is converted from int32 to
+            // float using the ConvertType. The Concatenate requires all columns to
+            // be of same type.
+            var pipeline = mlContext.Transforms.Conversion.ConvertType("Feature3",
+                outputKind: DataKind.Single)
+                .Append(mlContext.Transforms.Concatenate("Features", new[]
+                    { "Feature1", "Feature2", "Feature3" }));
 
             // The transformed data.
             var transformedData = pipeline.Fit(dataview).Transform(dataview);
 
             // Now let's take a look at what this concatenation did.
-            // We can extract the newly created column as an IEnumerable of TransformedData.
-            var featuresColumn = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, reuseRowObject: false);
+            // We can extract the newly created column as an IEnumerable of
+            // TransformedData.
+            var featuresColumn = mlContext.Data.CreateEnumerable<TransformedData>(
+                transformedData, reuseRowObject: false);
 
             // And we can write out a few rows
             Console.WriteLine($"Features column obtained post-transformation.");
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CopyColumns.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CopyColumns.cs
index 212292f087..a4a7498e0c 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CopyColumns.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CopyColumns.cs
@@ -8,40 +8,59 @@ public static class CopyColumns
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
             // Create a small dataset as an IEnumerable.
             var samples = new List<InputData>()
             {
-                new InputData(){ ImageId = 1, Features = new [] { 1.0f, 1.0f, 1.0f} },
-                new InputData(){ ImageId = 2, Features = new [] { 2.0f, 2.0f, 2.0f} },
-                new InputData(){ ImageId = 3, Features = new [] { 3.0f, 3.0f, 3.0f} },
-                new InputData(){ ImageId = 4, Features = new [] { 4.0f, 4.0f, 4.0f} },
-                new InputData(){ ImageId = 5, Features = new [] { 5.0f, 5.0f, 5.0f} },
-                new InputData(){ ImageId = 6, Features = new [] { 6.0f, 6.0f, 6.0f} },
+                new InputData(){ ImageId = 1, Features = new [] { 1.0f, 1.0f,
+                    1.0f } },
+
+                new InputData(){ ImageId = 2, Features = new [] { 2.0f, 2.0f,
+                    2.0f } },
+
+                new InputData(){ ImageId = 3, Features = new [] { 3.0f, 3.0f,
+                    3.0f } },
+
+                new InputData(){ ImageId = 4, Features = new [] { 4.0f, 4.0f,
+                    4.0f } },
+
+                new InputData(){ ImageId = 5, Features = new [] { 5.0f, 5.0f,
+                    5.0f } },
+
+                new InputData(){ ImageId = 6, Features = new [] { 6.0f, 6.0f,
+                    6.0f } },
             };
 
             // Convert training data to IDataView.
             var dataview = mlContext.Data.LoadFromEnumerable(samples);
 
             // CopyColumns is commonly used to rename columns.
-            // For example, if you want to train towards ImageId, and your trainer expects a "Label" column, you can
-            // use CopyColumns to rename ImageId to Label. Technically, the ImageId column still exists, but it won't be
-            // materialized unless you actually need it somewhere (e.g. if you were to save the transformed data
-            // without explicitly dropping the column). This is a general property of IDataView's lazy evaluation.
+            // For example, if you want to train towards ImageId, and your trainer
+            // expects a "Label" column, you can use CopyColumns to rename ImageId
+            // to Label. Technically, the ImageId column still exists, but it won't
+            // be materialized unless you actually need it somewhere (e.g. if you
+            // were to save the transformed data without explicitly dropping the
+            // column). This is a general property of IDataView's lazy evaluation.
             var pipeline = mlContext.Transforms.CopyColumns("Label", "ImageId");
 
-            // Now we can transform the data and look at the output to confirm the behavior of CopyColumns.
-            // Don't forget that this operation doesn't actually evaluate data until we read the data below.
+            // Now we can transform the data and look at the output to confirm the
+            // behavior of CopyColumns. Don't forget that this operation doesn't
+            // actually evaluate data until we read the data below.
             var transformedData = pipeline.Fit(dataview).Transform(dataview);
 
-            // We can extract the newly created column as an IEnumerable of SampleInfertDataTransformed, the class we define below.
-            var rowEnumerable = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, reuseRowObject: false);
+            // We can extract the newly created column as an IEnumerable of
+            // SampleInfertDataTransformed, the class we define below.
+            var rowEnumerable = mlContext.Data.CreateEnumerable<TransformedData>(
+                transformedData, reuseRowObject: false);
+
+            // And finally, we can write out the rows of the dataset, looking at the
+            // columns of interest.
+            Console.WriteLine($"Label and ImageId columns obtained " +
+                $"post-transformation.");
 
-            // And finally, we can write out the rows of the dataset, looking at the columns of interest.
-            Console.WriteLine($"Label and ImageId columns obtained post-transformation.");
             foreach (var row in rowEnumerable)
                 Console.WriteLine($"Label: {row.Label} ImageId: {row.ImageId}");
 
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CustomMapping.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CustomMapping.cs
index 9a0e7aabf3..c3ad19caaf 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CustomMapping.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CustomMapping.cs
@@ -8,8 +8,8 @@ public static class CustomMapping
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
             // Get a small dataset as an IEnumerable and convert it to an IDataView.
@@ -22,21 +22,27 @@ public static void Example()
             };
             var data = mlContext.Data.LoadFromEnumerable(samples);
 
-            // We define the custom mapping between input and output rows that will be applied by the transformation.
+            // We define the custom mapping between input and output rows that will
+            // be applied by the transformation.
             Action<InputData, CustomMappingOutput > mapping =
                 (input, output) => output.IsUnderThirty = input.Age < 30;
 
-            // Custom transformations can be used to transform data directly, or as part of a pipeline of estimators.
-            // Note: If contractName is null in the CustomMapping estimator, any pipeline of estimators containing it,
-            // cannot be saved and loaded back. 
-            var pipeline = mlContext.Transforms.CustomMapping(mapping, contractName: null);
+            // Custom transformations can be used to transform data directly, or as
+            // part of a pipeline of estimators. Note: If contractName is null in
+            // the CustomMapping estimator, any pipeline of estimators containing
+            // it, cannot be saved and loaded back. 
+            var pipeline = mlContext.Transforms.CustomMapping(mapping, contractName:
+                null);
 
-            // Now we can transform the data and look at the output to confirm the behavior of the estimator.
-            // This operation doesn't actually evaluate data until we read the data below.
+            // Now we can transform the data and look at the output to confirm the
+            // behavior of the estimator. This operation doesn't actually evaluate
+            // data until we read the data below.
             var transformer = pipeline.Fit(data);
             var transformedData = transformer.Transform(data);
 
-            var dataEnumerable = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, reuseRowObject: true);
+            var dataEnumerable = mlContext.Data.CreateEnumerable<TransformedData>(
+                transformedData, reuseRowObject: true);
+
             Console.WriteLine("Age\t IsUnderThirty");
             foreach (var row in dataEnumerable)
                 Console.WriteLine($"{row.Age}\t {row.IsUnderThirty}");
@@ -49,7 +55,8 @@ public static void Example()
             // 28       True
         }
 
-        // Defines only the column to be generated by the custom mapping transformation in addition to the columns already present.
+        // Defines only the column to be generated by the custom mapping
+        // transformation in addition to the columns already present.
         private class CustomMappingOutput
         {
             public bool IsUnderThirty { get; set; }
@@ -61,7 +68,8 @@ private class InputData
             public float Age { get; set; }
         }
 
-        // Defines the schema of the transformed data, which includes the new column IsUnderThirty.
+        // Defines the schema of the transformed data, which includes the new column
+        // IsUnderThirty.
         private class TransformedData : InputData
         {
             public bool IsUnderThirty { get; set; }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CustomMappingSaveAndLoad.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CustomMappingSaveAndLoad.cs
index 8bc4439190..745169ef4e 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CustomMappingSaveAndLoad.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CustomMappingSaveAndLoad.cs
@@ -9,8 +9,8 @@ public static class CustomMappingSaveAndLoad
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
             // Get a small dataset as an IEnumerable and convert it to an IDataView.
@@ -23,23 +23,35 @@ public static void Example()
             };
             var data = mlContext.Data.LoadFromEnumerable(samples);
 
-            // Custom transformations can be used to transform data directly, or as part of a pipeline of estimators.
-            var pipeline = mlContext.Transforms.CustomMapping(new IsUnderThirtyCustomAction().GetMapping(), contractName: "IsUnderThirty");
+            // Custom transformations can be used to transform data directly, or as
+            // part of a pipeline of estimators.
+            var pipeline = mlContext.Transforms.CustomMapping(new
+                IsUnderThirtyCustomAction().GetMapping(), contractName:
+                "IsUnderThirty");
+
             var transformer = pipeline.Fit(data);
 
-            // To save and load the CustomMapping estimator, the assembly in which the custom action is defined needs to be registered in the
-            // environment. The following registers the assembly where IsUnderThirtyCustomAction is defined.    
-            mlContext.ComponentCatalog.RegisterAssembly(typeof(IsUnderThirtyCustomAction).Assembly);
+            // To save and load the CustomMapping estimator, the assembly in which
+            // the custom action is defined needs to be registered in the
+            // environment. The following registers the assembly where
+            // IsUnderThirtyCustomAction is defined.    
+            mlContext.ComponentCatalog.RegisterAssembly(typeof(
+                IsUnderThirtyCustomAction).Assembly);
 
-            // Now the transform pipeline can be saved and loaded through the usual MLContext method. 
+            // Now the transform pipeline can be saved and loaded through the usual
+            // MLContext method. 
             mlContext.Model.Save(transformer, data.Schema, "customTransform.zip");
-            var loadedTransform = mlContext.Model.Load("customTransform.zip", out var inputSchema);
+            var loadedTransform = mlContext.Model.Load("customTransform.zip", out 
+                var inputSchema);
 
-            // Now we can transform the data and look at the output to confirm the behavior of the estimator.
-            // This operation doesn't actually evaluate data until we read the data below.
+            // Now we can transform the data and look at the output to confirm the
+            // behavior of the estimator. This operation doesn't actually evaluate
+            // data until we read the data below.
             var transformedData = loadedTransform.Transform(data);
 
-            var dataEnumerable = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, reuseRowObject: true);
+            var dataEnumerable = mlContext.Data.CreateEnumerable<TransformedData>(
+                transformedData, reuseRowObject: true);
+
             Console.WriteLine("Age\tIsUnderThirty");
             foreach (var row in dataEnumerable)
                 Console.WriteLine($"{row.Age}\t {row.IsUnderThirty}");
@@ -52,21 +64,25 @@ public static void Example()
             // 28       True
         }
 
-        // The custom action needs to implement the abstract class CustomMappingFactory, and needs to have attribute
-        // CustomMappingFactoryAttribute with argument equal to the contractName used to define the CustomMapping estimator
-        // which uses the action.
+        // The custom action needs to implement the abstract class
+        // CustomMappingFactory, and needs to have attribute
+        // CustomMappingFactoryAttribute with argument equal to the contractName
+        // used to define the CustomMapping estimator which uses the action.
         [CustomMappingFactoryAttribute("IsUnderThirty")]
-        private class IsUnderThirtyCustomAction : CustomMappingFactory<InputData, CustomMappingOutput>
+        private class IsUnderThirtyCustomAction : CustomMappingFactory<InputData,
+            CustomMappingOutput>
         {
-            // We define the custom mapping between input and output rows that will be applied by the transformation.
-            public static void CustomAction(InputData input, CustomMappingOutput output)
-                => output.IsUnderThirty = input.Age < 30;
+            // We define the custom mapping between input and output rows that will
+            // be applied by the transformation.
+            public static void CustomAction(InputData input, CustomMappingOutput
+                output) => output.IsUnderThirty = input.Age < 30;
 
             public override Action<InputData, CustomMappingOutput> GetMapping()
                 => CustomAction;
         }
 
-        // Defines only the column to be generated by the custom mapping transformation in addition to the columns already present.
+        // Defines only the column to be generated by the custom mapping
+        // transformation in addition to the columns already present.
         private class CustomMappingOutput
         {
             public bool IsUnderThirty { get; set; }
@@ -78,7 +94,8 @@ private class InputData
             public float Age { get; set; }
         }
 
-        // Defines the schema of the transformed data, which includes the new column IsUnderThirty.
+        // Defines the schema of the transformed data, which includes the new column
+        // IsUnderThirty.
         private class TransformedData : InputData
         {
             public bool IsUnderThirty { get; set; }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CustomMappingWithInMemoryCustomType.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CustomMappingWithInMemoryCustomType.cs
index 688c5d1fe5..37fd961258 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CustomMappingWithInMemoryCustomType.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CustomMappingWithInMemoryCustomType.cs
@@ -12,34 +12,46 @@ static public void Example()
         {
             var mlContext = new MLContext();
             // Build in-memory data.
-            var tribe = new List<AlienHero>() { new AlienHero("ML.NET", 2, 1000, 2000, 3000, 4000, 5000, 6000, 7000) };
+            var tribe = new List<AlienHero>() { new AlienHero("ML.NET", 2, 1000,
+                2000, 3000, 4000, 5000, 6000, 7000) };
 
             // Build a ML.NET pipeline and make prediction.
             var tribeDataView = mlContext.Data.LoadFromEnumerable(tribe);
-            var pipeline = mlContext.Transforms.CustomMapping(AlienFusionProcess.GetMapping(), contractName: null);
+            var pipeline = mlContext.Transforms.CustomMapping(AlienFusionProcess
+                .GetMapping(), contractName: null);
+
             var model = pipeline.Fit(tribeDataView);
             var tribeTransformed = model.Transform(tribeDataView);
 
             // Print out prediction produced by the model.
-            var firstAlien = mlContext.Data.CreateEnumerable<SuperAlienHero>(tribeTransformed, false).First();
-            Console.WriteLine($"We got a super alien with name {firstAlien.Name}, age {firstAlien.Merged.Age}, " +
-                $"height {firstAlien.Merged.Height}, weight {firstAlien.Merged.Weight}, and {firstAlien.Merged.HandCount} hands.");
+            var firstAlien = mlContext.Data.CreateEnumerable<SuperAlienHero>(
+                tribeTransformed, false).First();
+
+            Console.WriteLine("We got a super alien with name " + firstAlien.Name + 
+                ", age " + firstAlien.Merged.Age + ", " + "height " + firstAlien
+                .Merged.Height + ", weight  " + firstAlien.Merged.Weight + ", and "
+                + firstAlien.Merged.HandCount + " hands.");
 
             // Expected output:
             //   We got a super alien with name Super Unknown, age 4002, height 6000, weight 8000, and 10000 hands.
 
             // Create a prediction engine and print out its prediction.
-            var engine = mlContext.Model.CreatePredictionEngine<AlienHero, SuperAlienHero>(model);
+            var engine = mlContext.Model.CreatePredictionEngine<AlienHero,
+                SuperAlienHero>(model);
+
             var alien = new AlienHero("TEN.LM", 1, 2, 3, 4, 5, 6, 7, 8);
             var superAlien = engine.Predict(alien);
-            Console.Write($"We got a super alien with name {superAlien.Name}, age {superAlien.Merged.Age}, " +
-                $"height {superAlien.Merged.Height}, weight {superAlien.Merged.Weight}, and {superAlien.Merged.HandCount} hands.");
+            Console.Write("We got a super alien with name " + superAlien.Name +
+                ", age " + superAlien.Merged.Age + ", height " + 
+                superAlien.Merged.Height + ", weight " + superAlien.Merged.Weight +
+                ", and " + superAlien.Merged.HandCount + " hands.");
 
             // Expected output:
             //   We got a super alien with name Super Unknown, age 6, height 8, weight 10, and 12 hands.
         }
 
-        // A custom type which ML.NET doesn't know yet. Its value will be loaded as a DataView column in this test.
+        // A custom type which ML.NET doesn't know yet. Its value will be loaded as
+        // a DataView column in this test.
         private class AlienBody
         {
             public int Age { get; set; }
@@ -67,11 +79,12 @@ public AlienTypeAttributeAttribute(int raceId)
                 RaceId = raceId;
             }
 
-            // A function implicitly invoked by ML.NET when processing a custom type.
-            // It binds a DataViewType to a custom type plus its attributes.
+            // A function implicitly invoked by ML.NET when processing a custom
+            // type. It binds a DataViewType to a custom type plus its attributes.
             public override void Register()
             {
-                DataViewTypeManager.Register(new DataViewAlienBodyType(RaceId), typeof(AlienBody), new[] { this });
+                DataViewTypeManager.Register(new DataViewAlienBodyType(RaceId),
+                    typeof(AlienBody), new[] { this });
             }
 
             public override bool Equals(DataViewTypeAttribute other)
@@ -84,11 +97,14 @@ public override bool Equals(DataViewTypeAttribute other)
             public override int GetHashCode() => RaceId.GetHashCode();
         }
 
-        // A custom class with a type which ML.NET doesn't know yet. Its value will be loaded as a DataView row in this test.
-        // It will be the input of AlienFusionProcess.MergeBody(AlienHero, SuperAlienHero).
+        // A custom class with a type which ML.NET doesn't know yet. Its value will
+        // be loaded as a DataView row in this test. It will be the input of
+        // AlienFusionProcess.MergeBody(AlienHero, SuperAlienHero).
         //
-        // The members One> and Two" would be mapped to different types inside ML.NET type system because they
-        // have different AlienTypeAttributeAttribute's. For example, the column type of One would be DataViewAlienBodyType
+        // The members One> and Two" would be mapped to different types inside
+        // ML.NET type system because they have different 
+        // AlienTypeAttributeAttribute's. For example, the column type of One would
+        // be DataViewAlienBodyType
         // with RaceId=100.
         // </summary>
         private class AlienHero
@@ -110,11 +126,13 @@ public AlienHero()
 
             public AlienHero(string name,
                 int age, float height, float weight, int handCount,
-                int anotherAge, float anotherHeight, float anotherWeight, int anotherHandCount)
+                int anotherAge, float anotherHeight, float anotherWeight, int
+                    anotherHandCount)
             {
                 Name = "Unknown";
                 One = new AlienBody(age, height, weight, handCount);
-                Two = new AlienBody(anotherAge, anotherHeight, anotherWeight, anotherHandCount);
+                Two = new AlienBody(anotherAge, anotherHeight, anotherWeight,
+                    anotherHandCount);
             }
         }
 
@@ -142,7 +160,8 @@ public override int GetHashCode()
             }
         }
 
-        // The output type of processing AlienHero using AlienFusionProcess.MergeBody(AlienHero, SuperAlienHero).
+        // The output type of processing AlienHero using AlienFusionProcess
+        // .MergeBody(AlienHero, SuperAlienHero).
         private class SuperAlienHero
         {
             public string Name { get; set; }
@@ -157,7 +176,8 @@ public SuperAlienHero()
             }
         }
 
-        // The implementation of custom mapping is MergeBody. It accepts AlienHero and produces SuperAlienHero.
+        // The implementation of custom mapping is MergeBody. It accepts AlienHero
+        // and produces SuperAlienHero.
         private class AlienFusionProcess
         {
             public static void MergeBody(AlienHero input, SuperAlienHero output)
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/DropColumns.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/DropColumns.cs
index 17188bd928..110aa3b2ef 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/DropColumns.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/DropColumns.cs
@@ -8,19 +8,30 @@ public static class DropColumns
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
             // Create a small dataset as an IEnumerable.
             var samples = new List<InputData>()
             {
-                new InputData(){ Age = 21, Gender = "Male", Education = "BS", ExtraColumn = 1 },
-                new InputData(){ Age = 23, Gender = "Female", Education = "MBA", ExtraColumn = 2 },
-                new InputData(){ Age = 28, Gender = "Male", Education = "PhD", ExtraColumn = 3 },
-                new InputData(){ Age = 22, Gender = "Male", Education = "BS", ExtraColumn = 4 },
-                new InputData(){ Age = 23, Gender = "Female", Education = "MS", ExtraColumn = 5 },
-                new InputData(){ Age = 27, Gender = "Female", Education = "PhD", ExtraColumn = 6 },
+                new InputData(){ Age = 21, Gender = "Male", Education = "BS",
+                    ExtraColumn = 1 },
+
+                new InputData(){ Age = 23, Gender = "Female", Education = "MBA",
+                    ExtraColumn = 2 },
+
+                new InputData(){ Age = 28, Gender = "Male", Education = "PhD",
+                    ExtraColumn = 3 },
+
+                new InputData(){ Age = 22, Gender = "Male", Education = "BS",
+                    ExtraColumn = 4 },
+
+                new InputData(){ Age = 23, Gender = "Female", Education = "MS",
+                    ExtraColumn = 5 },
+
+                new InputData(){ Age = 27, Gender = "Female", Education = "PhD",
+                    ExtraColumn = 6 },
             };
 
             // Convert training data to IDataView.
@@ -30,31 +41,40 @@ public static void Example()
             var pipeline = mlContext.Transforms.DropColumns("ExtraColumn");
 
             // Now we can transform the data and look at the output.
-            // Don't forget that this operation doesn't actually operate on data until we perform an action that requires 
+            // Don't forget that this operation doesn't actually operate on data
+            // until we perform an action that requires 
             // the data to be materialized.
             var transformedData = pipeline.Fit(dataview).Transform(dataview);
 
             // Now let's take a look at what the DropColumns operations did.
-            // We can extract the transformed data as an IEnumerable of InputData, the class we define below.
-            // When we try to pull out the Age, Gender, Education and ExtraColumn columns, ML.NET will raise an exception on the ExtraColumn
+            // We can extract the transformed data as an IEnumerable of InputData,
+            // the class we define below. When we try to pull out the Age, Gender,
+            // Education and ExtraColumn columns, ML.NET will raise an exception on
+            // the ExtraColumn
             try
             {
-                var failingRowEnumerable = mlContext.Data.CreateEnumerable<InputData>(transformedData, reuseRowObject: false);
+                var failingRowEnumerable = mlContext.Data.CreateEnumerable<
+                    InputData>(transformedData, reuseRowObject: false);
             }
             catch (ArgumentOutOfRangeException exception)
             {
-                Console.WriteLine($"ExtraColumn is not available, so an exception is thrown: {exception.Message}.");
+                Console.WriteLine($"ExtraColumn is not available, so an exception" +
+                    $" is thrown: {exception.Message}.");
             }
 
             // Expected output:
             //  ExtraColumn is not available, so an exception is thrown: Could not find  column 'ExtraColumn'.
             //  Parameter name: Schema
 
-            // And we can write a few columns out to see that the rest of the data is still available.
-            var rowEnumerable = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, reuseRowObject: false);
+            // And we can write a few columns out to see that the rest of the data
+            // is still available.
+            var rowEnumerable = mlContext.Data.CreateEnumerable<TransformedData>(
+                transformedData, reuseRowObject: false);
+
             Console.WriteLine($"The columns we didn't drop are still available.");
             foreach (var row in rowEnumerable)
-                Console.WriteLine($"Age: {row.Age} Gender: {row.Gender} Education: {row.Education}");
+                Console.WriteLine($"Age: {row.Age} Gender: {row.Gender} " +
+                    $"Education: {row.Education}");
 
             // Expected output:
             //  The columns we didn't drop are still available.
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/IndicateMissingValues.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/IndicateMissingValues.cs
index 7e880b90d5..b4eb2bf21e 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/IndicateMissingValues.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/IndicateMissingValues.cs
@@ -10,8 +10,8 @@ public static class IndicateMissingValues
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
             // Get a small dataset as an IEnumerable and convert it to an IDataView.
@@ -23,21 +23,29 @@ public static void Example()
             };
             var data = mlContext.Data.LoadFromEnumerable(samples);
 
-            // IndicateMissingValues is used to create a boolean containing 'true' where the value in the 
-            // input column is missing. For floats and doubles, missing values are represented as NaN.
-            var pipeline = mlContext.Transforms.IndicateMissingValues("MissingIndicator", "Features");
+            // IndicateMissingValues is used to create a boolean containing 'true'
+            // where the value in the input column is missing. For floats and
+            // doubles, missing values are represented as NaN.
+            var pipeline = mlContext.Transforms.IndicateMissingValues(
+                "MissingIndicator", "Features");
 
-            // Now we can transform the data and look at the output to confirm the behavior of the estimator.
-            // This operation doesn't actually evaluate data until we read the data below.
+            // Now we can transform the data and look at the output to confirm the
+            // behavior of the estimator. This operation doesn't actually evaluate
+            // data until we read the data below.
             var tansformer = pipeline.Fit(data);
             var transformedData = tansformer.Transform(data);
 
-            // We can extract the newly created column as an IEnumerable of SampleDataTransformed, the class we define below.
-            var rowEnumerable = mlContext.Data.CreateEnumerable<SampleDataTransformed>(transformedData, reuseRowObject: false);
+            // We can extract the newly created column as an IEnumerable of
+            // SampleDataTransformed, the class we define below.
+            var rowEnumerable = mlContext.Data.CreateEnumerable<
+                SampleDataTransformed>(transformedData, reuseRowObject: false);
 
-            // And finally, we can write out the rows of the dataset, looking at the columns of interest.
+            // And finally, we can write out the rows of the dataset, looking at the
+            // columns of interest.
             foreach (var row in rowEnumerable)
-                Console.WriteLine($"Features: [{string.Join(", ", row.Features)}]\t MissingIndicator: [{string.Join(", ", row.MissingIndicator)}]");
+                Console.WriteLine("Features: [" + string.Join(", ", row.Features) +
+                    "]\t MissingIndicator: [" + string.Join(", ", row
+                    .MissingIndicator) + "]");
 
             // Expected output:
             // Features: [1, 1, 0]      MissingIndicator: [False, False, False]
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/IndicateMissingValuesMultiColumn.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/IndicateMissingValuesMultiColumn.cs
index 830fb9d047..38750ed0a6 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/IndicateMissingValuesMultiColumn.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/IndicateMissingValuesMultiColumn.cs
@@ -9,40 +9,53 @@ public static class IndicateMissingValuesMultiColumn
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
             // Get a small dataset as an IEnumerable and convert it to an IDataView.
             var samples = new List<DataPoint>()
             {
-                new DataPoint(){ Features1 = new float[3] {1, 1, 0}, Features2 = new float[2] {1, 1} },
-                new DataPoint(){ Features1 = new float[3] {0, float.NaN, 1}, Features2 = new float[2] {float.NaN, 1} },
-                new DataPoint(){ Features1 = new float[3] {-1, float.NaN, -3}, Features2 = new float[2] {1, float.PositiveInfinity} },
+                new DataPoint(){ Features1 = new float[3] {1, 1, 0}, Features2 =
+                    new float[2] {1, 1} },
+
+                new DataPoint(){ Features1 = new float[3] {0, float.NaN, 1},
+                    Features2 = new float[2] {float.NaN, 1} },
+
+                new DataPoint(){ Features1 = new float[3] {-1, float.NaN, -3},
+                    Features2 = new float[2] {1, float.PositiveInfinity} },
             };
             var data = mlContext.Data.LoadFromEnumerable(samples);
 
-            // IndicateMissingValues is used to create a boolean containing 'true' where the value in the 
-            // input column is missing. For floats and doubles, missing values are NaN.
-            // We can use an array of InputOutputColumnPair to apply the MissingValueIndicatorEstimator
+            // IndicateMissingValues is used to create a boolean containing 'true'
+            // where the value in the input column is missing. For floats and
+            // doubles, missing values are NaN. We can use an array of
+            // InputOutputColumnPair to apply the MissingValueIndicatorEstimator
             // to multiple columns in one pass over the data.
             var pipeline = mlContext.Transforms.IndicateMissingValues(new[] {
                 new InputOutputColumnPair("MissingIndicator1", "Features1"),
                 new InputOutputColumnPair("MissingIndicator2", "Features2")
             });
 
-            // Now we can transform the data and look at the output to confirm the behavior of the estimator.
-            // This operation doesn't actually evaluate data until we read the data below.
+            // Now we can transform the data and look at the output to confirm the
+            // behavior of the estimator. This operation doesn't actually evaluate
+            // data until we read the data below.
             var tansformer = pipeline.Fit(data);
             var transformedData = tansformer.Transform(data);
 
-            // We can extract the newly created column as an IEnumerable of SampleDataTransformed, the class we define below.
-            var rowEnumerable = mlContext.Data.CreateEnumerable<SampleDataTransformed>(transformedData, reuseRowObject: false);
+            // We can extract the newly created column as an IEnumerable of
+            // SampleDataTransformed, the class we define below.
+            var rowEnumerable = mlContext.Data.CreateEnumerable<
+                SampleDataTransformed>(transformedData, reuseRowObject: false);
 
-            // And finally, we can write out the rows of the dataset, looking at the columns of interest.
+            // And finally, we can write out the rows of the dataset, looking at the
+            // columns of interest.
             foreach (var row in rowEnumerable)
-                Console.WriteLine($"Features1: [{string.Join(", ", row.Features1)}]\t MissingIndicator1: [{string.Join(", ", row.MissingIndicator1)}]\t " +
-                    $"Features2: [{string.Join(", ", row.Features2)}]\t MissingIndicator2: [{string.Join(", ", row.MissingIndicator2)}]");
+                Console.WriteLine("Features1: [" + string.Join(", ", row
+                    .Features1) + "]\t MissingIndicator1: [" + string.Join(", ",
+                    row.MissingIndicator1) + "]\t Features2: [" + string.Join(", ",
+                    row.Features2) + "]\t MissingIndicator2: [" + string.Join(", ",
+                    row.MissingIndicator2) + "]");
 
             // Expected output:
             // Features1: [1, 1, 0]     MissingIndicator1: [False, False, False]        Features2: [1, 1]       MissingIndicator2: [False, False]
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeBinning.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeBinning.cs
index ba23d87e2f..27315443c7 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeBinning.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeBinning.cs
@@ -12,7 +12,8 @@ public class NormalizeBinning
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, 
             // as well as the source of randomness.
             var mlContext = new MLContext();
             var samples = new List<DataPoint>()
@@ -22,60 +23,91 @@ public static void Example()
                 new DataPoint(){ Features = new float[4] { 4, 0, 1, 0} },
                 new DataPoint(){ Features = new float[4] { 2,-1,-1, 1} }
             };
-            // Convert training data to IDataView, the general data type used in ML.NET.
+            // Convert training data to IDataView, the general data type used in
+            // ML.NET.
             var data = mlContext.Data.LoadFromEnumerable(samples);
-            // NormalizeBinning normalizes the data by constructing equidensity bins and produce output based on 
+            // NormalizeBinning normalizes the data by constructing equidensity bins
+            // and produce output based on 
             // to which bin the original value belongs.
-            var normalize = mlContext.Transforms.NormalizeBinning("Features", maximumBinCount: 4, fixZero: false);
+            var normalize = mlContext.Transforms.NormalizeBinning("Features",
+                maximumBinCount: 4, fixZero: false);
 
-            // NormalizeBinning normalizes the data by constructing equidensity bins and produce output based on 
-            // to which bin original value belong but make sure zero values would remain zero after normalization.
-            // Helps preserve sparsity.
-            var normalizeFixZero = mlContext.Transforms.NormalizeBinning("Features", maximumBinCount: 4, fixZero: true);
+            // NormalizeBinning normalizes the data by constructing equidensity bins
+            // and produce output based on to which bin original value belong but
+            // make sure zero values would remain zero after normalization. Helps
+            // preserve sparsity.
+            var normalizeFixZero = mlContext.Transforms.NormalizeBinning("Features",
+                maximumBinCount: 4, fixZero: true);
 
-            // Now we can transform the data and look at the output to confirm the behavior of the estimator.
-            // This operation doesn't actually evaluate data until we read the data below.
+            // Now we can transform the data and look at the output to confirm the
+            // behavior of the estimator. This operation doesn't actually evaluate
+            // data until we read the data below.
             var normalizeTransform = normalize.Fit(data);
             var transformedData = normalizeTransform.Transform(data);
             var normalizeFixZeroTransform = normalizeFixZero.Fit(data);
             var fixZeroData = normalizeFixZeroTransform.Transform(data);
             var column = transformedData.GetColumn<float[]>("Features").ToArray();
             foreach (var row in column)
-                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString("f4"))));
+                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString(
+                    "f4"))));
             // Expected output:
             //  1.0000, 0.6667, 1.0000, 0.0000
             //  0.6667, 1.0000, 0.6667, 0.0000
             //  0.3333, 0.3333, 0.3333, 0.0000
             //  0.0000, 0.0000, 0.0000, 1.0000
 
-            var columnFixZero = fixZeroData.GetColumn<float[]>("Features").ToArray();
+            var columnFixZero = fixZeroData.GetColumn<float[]>("Features")
+                .ToArray();
+
             foreach (var row in columnFixZero)
-                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString("f4"))));
+                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString(
+                    "f4"))));
             // Expected output:
             //  1.0000, 0.3333, 1.0000, 0.0000
             //  0.6667, 0.6667, 0.6667, 0.0000
             //  0.3333, 0.0000, 0.3333, 0.0000
             //  0.0000, -0.3333, 0.0000, 1.0000
 
-            // Let's get transformation parameters. Since we work with only one column we need to pass 0 as parameter for GetNormalizerModelParameters.
-            // If we have multiple columns transformations we need to pass index of InputOutputColumnPair.
-            var transformParams = normalizeTransform.GetNormalizerModelParameters(0) as BinNormalizerModelParameters<ImmutableArray<float>>;
+            // Let's get transformation parameters. Since we work with only one
+            // column we need to pass 0 as parameter for
+            // GetNormalizerModelParameters. If we have multiple columns
+            // transformations we need to pass index of InputOutputColumnPair.
+            var transformParams = normalizeTransform.GetNormalizerModelParameters(0)
+                as BinNormalizerModelParameters<ImmutableArray<float>>;
+
             var density = transformParams.Density[0];
-            var offset = (transformParams.Offset.Length == 0 ? 0 : transformParams.Offset[0]);
-            Console.WriteLine($"The 0-index value in resulting array would be produce by: y = (Index(x) / {density}) - {offset}");
-            Console.WriteLine("Where Index(x) is the index of the bin to which x belongs");
-            Console.WriteLine($"Bins upper bounds are: {string.Join(" ", transformParams.UpperBounds[0])}");
+            var offset = (transformParams.Offset.Length == 0 ? 0 : transformParams
+                .Offset[0]);
+
+            Console.WriteLine($"The 0-index value in resulting array would be " +
+                $"produce by: y = (Index(x) / {density}) - {offset}");
+
+            Console.WriteLine("Where Index(x) is the index of the bin to which " +
+                "x belongs");
+
+            Console.WriteLine("Bins upper bounds are: " + string.Join(" ",
+                transformParams.UpperBounds[0]));
             // Expected output:
             //  The 0-index value in resulting array would be produce by: y = (Index(x) / 3) - 0
             //  Where Index(x) is the index of the bin to which x belongs
             //  Bins upper bounds are: 3 5 7 ∞
 
-            var fixZeroParams = (normalizeFixZeroTransform.GetNormalizerModelParameters(0) as BinNormalizerModelParameters<ImmutableArray<float>>);
+            var fixZeroParams = (normalizeFixZeroTransform
+                .GetNormalizerModelParameters(0) as BinNormalizerModelParameters<
+                ImmutableArray<float>>);
+
             density = fixZeroParams.Density[1];
-            offset = (fixZeroParams.Offset.Length == 0 ? 0 : fixZeroParams.Offset[1]);
-            Console.WriteLine($"The 0-index value in resulting array would be produce by: y = (Index(x) / {density}) - {offset}");
-            Console.WriteLine("Where Index(x) is the index of the bin to which x belongs");
-            Console.WriteLine($"Bins upper bounds are: {string.Join(" ", fixZeroParams.UpperBounds[1])}");
+            offset = (fixZeroParams.Offset.Length == 0 ? 0 : fixZeroParams
+                .Offset[1]);
+
+            Console.WriteLine($"The 0-index value in resulting array would be " +
+                $"produce by: y = (Index(x) / {density}) - {offset}");
+
+            Console.WriteLine("Where Index(x) is the index of the bin to which x " +
+                "belongs");
+
+            Console.WriteLine("Bins upper bounds are: " + string.Join(" ",
+                fixZeroParams.UpperBounds[1]));
             // Expected output:
             //  The 0-index value in resulting array would be produce by: y = (Index(x) / 3) - 0.3333333
             //  Where Index(x) is the index of the bin to which x belongs
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeBinningMulticolumn.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeBinningMulticolumn.cs
index 90b9360aa4..9d6ef29ed7 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeBinningMulticolumn.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeBinningMulticolumn.cs
@@ -12,35 +12,45 @@ public class NormalizeBinningMulticolumn
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
             var samples = new List<DataPoint>()
             {
-                new DataPoint(){ Features = new float[4] { 8, 1, 3, 0}, Features2 = 1 },
-                new DataPoint(){ Features = new float[4] { 6, 2, 2, 0}, Features2 = 4 },
-                new DataPoint(){ Features = new float[4] { 4, 0, 1, 0}, Features2 = 1 },
-                new DataPoint(){ Features = new float[4] { 2,-1,-1, 1}, Features2 = 2 }
+                new DataPoint(){ Features = new float[4] { 8, 1, 3, 0},
+                    Features2 = 1 },
+
+                new DataPoint(){ Features = new float[4] { 6, 2, 2, 0},
+                    Features2 = 4 },
+
+                new DataPoint(){ Features = new float[4] { 4, 0, 1, 0},
+                    Features2 = 1 },
+
+                new DataPoint(){ Features = new float[4] { 2,-1,-1, 1},
+                    Features2 = 2 }
             };
-            // Convert training data to IDataView, the general data type used in ML.NET.
+            // Convert training data to IDataView, the general data type used in
+            // ML.NET.
             var data = mlContext.Data.LoadFromEnumerable(samples);
-            // NormalizeBinning normalizes the data by constructing equidensity bins and produce output based on 
-            // to which bin the original value belongs.
+            // NormalizeBinning normalizes the data by constructing equidensity bins
+            // and produce output based on to which bin the original value belongs.
             var normalize = mlContext.Transforms.NormalizeBinning(new[]{
                 new InputOutputColumnPair("Features"),
                 new InputOutputColumnPair("Features2"),
                 },
                 maximumBinCount: 4, fixZero: false);
 
-            // Now we can transform the data and look at the output to confirm the behavior of the estimator.
-            // This operation doesn't actually evaluate data until we read the data below.
+            // Now we can transform the data and look at the output to confirm the
+            // behavior of the estimator. This operation doesn't actually evaluate
+            // data until we read the data below.
             var normalizeTransform = normalize.Fit(data);
             var transformedData = normalizeTransform.Transform(data);
             var column = transformedData.GetColumn<float[]>("Features").ToArray();
             var column2 = transformedData.GetColumn<float>("Features2").ToArray();
 
             for(int i=0; i< column.Length; i++)
-                Console.WriteLine(string.Join(", ", column[i].Select(x => x.ToString("f4")))+"\t\t"+column2[i]);
+                Console.WriteLine(string.Join(", ", column[i].Select(x => x
+                .ToString("f4")))+"\t\t"+column2[i]);
             // Expected output:
             //
             //  Features                            Feature2
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeGlobalContrast.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeGlobalContrast.cs
index 322997ca3b..8d4795a1b6 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeGlobalContrast.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeGlobalContrast.cs
@@ -10,8 +10,8 @@ class NormalizeGlobalContrast
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
             var samples = new List<DataPoint>()
             {
@@ -20,18 +20,23 @@ public static void Example()
                 new DataPoint(){ Features = new float[4] { 1, 0, 1, 0} },
                 new DataPoint(){ Features = new float[4] { 0, 1, 0, 1} }
             };
-            // Convert training data to IDataView, the general data type used in ML.NET.
+            // Convert training data to IDataView, the general data type used in
+            // ML.NET.
             var data = mlContext.Data.LoadFromEnumerable(samples);
-            var approximation = mlContext.Transforms.NormalizeGlobalContrast("Features", ensureZeroMean: false, scale:2, ensureUnitStandardDeviation:true);
+            var approximation = mlContext.Transforms.NormalizeGlobalContrast(
+                "Features", ensureZeroMean: false, scale:2, 
+                ensureUnitStandardDeviation:true);
 
-            // Now we can transform the data and look at the output to confirm the behavior of the estimator.
-            // This operation doesn't actually evaluate data until we read the data below.
+            // Now we can transform the data and look at the output to confirm the
+            // behavior of the estimator. This operation doesn't actually evaluate
+            // data until we read the data below.
             var tansformer = approximation.Fit(data);
             var transformedData = tansformer.Transform(data);
 
             var column = transformedData.GetColumn<float[]>("Features").ToArray();
             foreach (var row in column)
-                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString("f4"))));
+                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString(
+                    "f4"))));
             // Expected output:
             //  2.0000, 2.0000,-2.0000,-2.0000
             //  2.0000, 2.0000,-2.0000,-2.0000
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeLogMeanVariance.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeLogMeanVariance.cs
index 37d302c022..3ef43d9f85 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeLogMeanVariance.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeLogMeanVariance.cs
@@ -12,8 +12,8 @@ public class NormalizeLogMeanVariance
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
             var samples = new List<DataPoint>()
             {
@@ -22,24 +22,32 @@ public static void Example()
                 new DataPoint(){ Features = new float[5] { 0, 0, 1, 0, 0} },
                 new DataPoint(){ Features = new float[5] {-1,-1,-1, 1, 1} }
             };
-            // Convert training data to IDataView, the general data type used in ML.NET.
+            // Convert training data to IDataView, the general data type used in
+            // ML.NET.
             var data = mlContext.Data.LoadFromEnumerable(samples);
-            // NormalizeLogMeanVariance normalizes the data based on the computed mean and variance of the logarithm of the data.
+            // NormalizeLogMeanVariance normalizes the data based on the computed
+            // mean and variance of the logarithm of the data.
             // Uses Cumulative distribution function as output.
-            var normalize = mlContext.Transforms.NormalizeLogMeanVariance("Features", useCdf: true);
+            var normalize = mlContext.Transforms.NormalizeLogMeanVariance(
+                "Features", useCdf: true);
 
-            // NormalizeLogMeanVariance normalizes the data based on the computed mean and variance of the logarithm of the data.
-            var normalizeNoCdf = mlContext.Transforms.NormalizeLogMeanVariance("Features", useCdf: false);
+            // NormalizeLogMeanVariance normalizes the data based on the computed
+            // mean and variance of the logarithm of the data.
+            var normalizeNoCdf = mlContext.Transforms.NormalizeLogMeanVariance(
+                "Features", useCdf: false);
 
-            // Now we can transform the data and look at the output to confirm the behavior of the estimator.
-            // This operation doesn't actually evaluate data until we read the data below.
+            // Now we can transform the data and look at the output to confirm the
+            // behavior of the estimator.
+            // This operation doesn't actually evaluate data until we read the data
+            // below.
             var normalizeTransform = normalize.Fit(data);
             var transformedData = normalizeTransform.Transform(data);
             var normalizeNoCdfTransform = normalizeNoCdf.Fit(data);
             var noCdfData = normalizeNoCdfTransform.Transform(data);
             var column = transformedData.GetColumn<float[]>("Features").ToArray();
             foreach (var row in column)
-                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString("f4"))));
+                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString(
+                    "f4"))));
             // Expected output:
             //  0.1587, 0.1587, 0.8654, 0.0000, 0.8413
             //  0.8413, 0.8413, 0.5837, 0.0000, 0.0000
@@ -48,27 +56,38 @@ public static void Example()
 
             var columnFixZero = noCdfData.GetColumn<float[]>("Features").ToArray();
             foreach (var row in columnFixZero)
-                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString("f4"))));
+                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString(
+                    "f4"))));
             // Expected output:
             //  1.8854, 1.8854, 5.2970, 0.0000, 7670682000000000000000000000000000000.0000
             //  4.7708, 4.7708, 3.0925, 0.0000, -7670682000000000000000000000000000000.0000
             // -1.0000,-1.0000, 0.8879, 0.0000, -1.0000
             // -3.8854,-3.8854,-3.5213, 0.0000, -0.9775
 
-            // Let's get transformation parameters. Since we work with only one column we need to pass 0 as parameter for GetNormalizerModelParameters.
-            // If we have multiple columns transformations we need to pass index of InputOutputColumnPair.
-            var transformParams = normalizeTransform.GetNormalizerModelParameters(0) as CdfNormalizerModelParameters<ImmutableArray<float>>;
-            Console.WriteLine("The 1-index value in resulting array would be produce by:");
-            Console.WriteLine($"y = 0.5* (1 + ERF((Math.Log(x)- {transformParams.Mean[1]}) / ({transformParams.StandardDeviation[1]} * sqrt(2)))");
+            // Let's get transformation parameters. Since we work with only one
+            // column we need to pass 0 as parameter for
+            // GetNormalizerModelParameters. If we have multiple columns
+            // transformations we need to pass index of InputOutputColumnPair.
+            var transformParams = normalizeTransform.GetNormalizerModelParameters(0)
+                as CdfNormalizerModelParameters<ImmutableArray<float>>;
+
+            Console.WriteLine("The 1-index value in resulting array would be " +
+                "produce by:");
+
+            Console.WriteLine("y = 0.5* (1 + ERF((Math.Log(x)- " + transformParams
+                .Mean[1] + ") / (" + transformParams.StandardDeviation[1] + 
+                " * sqrt(2)))");
 
             // ERF is https://en.wikipedia.org/wiki/Error_function.
             // Expected output:
             //  The 1-index value in resulting array would be produce by:
-            //  y = 0.5 * (1 + ERF((Math.Log(x) - 0.3465736) / (0.3465736 * sqrt(2)))
-            var noCdfParams = normalizeNoCdfTransform.GetNormalizerModelParameters(0) as AffineNormalizerModelParameters<ImmutableArray<float>>;
+            //  y = 0.5* (1 + ERF((Math.Log(x)- 0.3465736) / (0.3465736 * sqrt(2)))
+            var noCdfParams = normalizeNoCdfTransform.GetNormalizerModelParameters(
+                0) as AffineNormalizerModelParameters<ImmutableArray<float>>;
             var offset = noCdfParams.Offset.Length == 0 ? 0 : noCdfParams.Offset[1];
             var scale = noCdfParams.Scale[1];
-            Console.WriteLine($"The 1-index value in resulting array would be produce by: y = (x - ({offset})) * {scale}");
+            Console.WriteLine($"The 1-index value in resulting array would be " +
+                $"produce by: y = (x - ({offset})) * {scale}");
             // Expected output:
             // The 1-index value in resulting array would be produce by: y = (x - (0.3465736)) * 2.88539
         }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeLpNorm.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeLpNorm.cs
index 83883ae49e..c6a020b850 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeLpNorm.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeLpNorm.cs
@@ -11,8 +11,8 @@ class NormalizeLpNorm
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
             var samples = new List<DataPoint>()
             {
@@ -21,18 +21,23 @@ public static void Example()
                 new DataPoint(){ Features = new float[4] { 1, 0, 1, 0} },
                 new DataPoint(){ Features = new float[4] { 0, 1, 0, 1} }
             };
-            // Convert training data to IDataView, the general data type used in ML.NET.
+            // Convert training data to IDataView, the general data type used in
+            // ML.NET.
             var data = mlContext.Data.LoadFromEnumerable(samples);
-            var approximation = mlContext.Transforms.NormalizeLpNorm("Features", norm: LpNormNormalizingEstimatorBase.NormFunction.L1, ensureZeroMean: true);
+            var approximation = mlContext.Transforms.NormalizeLpNorm("Features",
+                norm: LpNormNormalizingEstimatorBase.NormFunction.L1,
+                ensureZeroMean: true);
 
-            // Now we can transform the data and look at the output to confirm the behavior of the estimator.
-            // This operation doesn't actually evaluate data until we read the data below.
+            // Now we can transform the data and look at the output to confirm the
+            // behavior of the estimator. This operation doesn't actually evaluate
+            // data until we read the data below.
             var tansformer = approximation.Fit(data);
             var transformedData = tansformer.Transform(data);
 
             var column = transformedData.GetColumn<float[]>("Features").ToArray();
             foreach (var row in column)
-                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString("f4"))));
+                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString(
+                    "f4"))));
             // Expected output:
             //  0.2500,  0.2500, -0.2500, -0.2500
             //  0.2500,  0.2500, -0.2500, -0.2500
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeMeanVariance.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeMeanVariance.cs
index ad35d43e6f..cccbb58f1b 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeMeanVariance.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeMeanVariance.cs
@@ -12,8 +12,8 @@ public class NormalizeMeanVariance
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
             var samples = new List<DataPoint>()
             {
@@ -22,24 +22,31 @@ public static void Example()
                 new DataPoint(){ Features = new float[4] { 0, 0, 1, 0} },
                 new DataPoint(){ Features = new float[4] {-1,-1,-1, 1} }
             };
-            // Convert training data to IDataView, the general data type used in ML.NET.
+            // Convert training data to IDataView, the general data type used in
+            // ML.NET.
             var data = mlContext.Data.LoadFromEnumerable(samples);
-            // NormalizeMeanVariance normalizes the data based on the computed mean and variance of the data.
-            // Uses Cumulative distribution function as output.
-            var normalize = mlContext.Transforms.NormalizeMeanVariance("Features", useCdf: true);
+            // NormalizeMeanVariance normalizes the data based on the computed mean
+            // and variance of the data. Uses Cumulative distribution function as
+            // output.
+            var normalize = mlContext.Transforms.NormalizeMeanVariance("Features",
+                useCdf: true);
 
-            // NormalizeMeanVariance normalizes the data based on the computed mean and variance of the data.
-            var normalizeNoCdf = mlContext.Transforms.NormalizeMeanVariance("Features", useCdf: false);
+            // NormalizeMeanVariance normalizes the data based on the computed mean
+            // and variance of the data.
+            var normalizeNoCdf = mlContext.Transforms.NormalizeMeanVariance(
+                "Features", useCdf: false);
 
-            // Now we can transform the data and look at the output to confirm the behavior of the estimator.
-            // This operation doesn't actually evaluate data until we read the data below.
+            // Now we can transform the data and look at the output to confirm the
+            // behavior of the estimator. This operation doesn't actually evaluate
+            // data until we read the data below.
             var normalizeTransform = normalize.Fit(data);
             var transformedData = normalizeTransform.Transform(data);
             var normalizeNoCdfTransform = normalizeNoCdf.Fit(data);
             var noCdfData = normalizeNoCdfTransform.Transform(data);
             var column = transformedData.GetColumn<float[]>("Features").ToArray();
             foreach (var row in column)
-                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString("f4"))));
+                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString(
+                    "f4"))));
             // Expected output:
             //  0.6726, 0.6726, 0.8816, 0.2819
             //  0.9101, 0.9101, 0.6939, 0.2819
@@ -49,27 +56,40 @@ public static void Example()
 
             var columnFixZero = noCdfData.GetColumn<float[]>("Features").ToArray();
             foreach (var row in columnFixZero)
-                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString("f4"))));
+                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString(
+                    "f4"))));
             // Expected output:
             //  0.8165, 0.8165, 1.5492, 0.0000
             //  1.6330, 1.6330, 1.0328, 0.0000
             //  0.0000, 0.0000, 0.5164, 0.0000
             // -0.8165,-0.8165,-0.5164, 2.0000
 
-            // Let's get transformation parameters. Since we work with only one column we need to pass 0 as parameter for GetNormalizerModelParameters.
-            // If we have multiple columns transformations we need to pass index of InputOutputColumnPair.
-            var transformParams = normalizeTransform.GetNormalizerModelParameters(0) as CdfNormalizerModelParameters<ImmutableArray<float>>;
-            Console.WriteLine($"The 1-index value in resulting array would be produce by:");
-            Console.WriteLine($" y = 0.5* (1 + ERF((x- {transformParams.Mean[1]}) / ({transformParams.StandardDeviation[1]} * sqrt(2)))");
+            // Let's get transformation parameters. Since we work with only one
+            // column we need to pass 0 as parameter for
+            // GetNormalizerModelParameters. If we have multiple columns
+            // transformations we need to pass index of InputOutputColumnPair.
+            var transformParams = normalizeTransform
+                .GetNormalizerModelParameters(0) as CdfNormalizerModelParameters<
+                ImmutableArray<float>>;
+
+            Console.WriteLine($"The 1-index value in resulting array would " +
+                $"be produce by:");
+
+            Console.WriteLine(" y = 0.5* (1 + ERF((x- " + transformParams.Mean[1] +
+                ") / (" + transformParams.StandardDeviation[1] + " * sqrt(2)))");
             // ERF is https://en.wikipedia.org/wiki/Error_function.
             // Expected output:
             //  The 1-index value in resulting array would be produce by:
             //  y = 0.5 * (1 + ERF((x - 0.5) / (1.118034 * sqrt(2)))
 
-            var noCdfParams = normalizeNoCdfTransform.GetNormalizerModelParameters(0) as AffineNormalizerModelParameters<ImmutableArray<float>>;
+            var noCdfParams = normalizeNoCdfTransform
+                .GetNormalizerModelParameters(0) as 
+                AffineNormalizerModelParameters<ImmutableArray<float>>;
+
             var offset = noCdfParams.Offset.Length == 0 ? 0 : noCdfParams.Offset[1];
             var scale = noCdfParams.Scale[1];
-            Console.WriteLine($"Values for slot 1 would be transfromed by applying y = (x - ({offset})) * {scale}");
+            Console.WriteLine($"Values for slot 1 would be transfromed by " +
+                $"applying y = (x - ({offset})) * {scale}");
             // Expected output:
             // The 1-index value in resulting array would be produce by: y = (x - (0)) * 0.8164966
         }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeMinMax.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeMinMax.cs
index 7b7a60d74e..f78056f9e6 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeMinMax.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeMinMax.cs
@@ -12,8 +12,8 @@ public class NormalizeMinMax
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
             var samples = new List<DataPoint>()
             {
@@ -22,46 +22,63 @@ public static void Example()
                 new DataPoint(){ Features = new float[4] { 0, 0, 1, 0} },
                 new DataPoint(){ Features = new float[4] {-1,-1,-1, 1} }
             };
-            // Convert training data to IDataView, the general data type used in ML.NET.
+            // Convert training data to IDataView, the general data type used in
+            // ML.NET.
             var data = mlContext.Data.LoadFromEnumerable(samples);
-            // NormalizeMinMax normalize rows by finding min and max values in each row slot
-            // and setting projection of min value to 0 and max to 1 and everything else to
-            // values in between.
-            var normalize = mlContext.Transforms.NormalizeMinMax("Features", fixZero: false);
+            // NormalizeMinMax normalize rows by finding min and max values in each
+            // row slot and setting projection of min value to 0 and max to 1 and
+            // everything else to values in between.
+            var normalize = mlContext.Transforms.NormalizeMinMax("Features",
+                fixZero: false);
 
-            // Normalize rows by finding min and max values in each row slot, but make sure
-            // zero values would remain zero after normalization. Helps preserve sparsity.
-            var normalizeFixZero = mlContext.Transforms.NormalizeMinMax("Features", fixZero: true);
+            // Normalize rows by finding min and max values in each row slot, but
+            // make sure zero values would remain zero after normalization. Helps
+            // preserve sparsity.
+            var normalizeFixZero = mlContext.Transforms.NormalizeMinMax("Features",
+                fixZero: true);
 
-            // Now we can transform the data and look at the output to confirm the behavior of the estimator.
-            // This operation doesn't actually evaluate data until we read the data below.
+            // Now we can transform the data and look at the output to confirm the
+            // behavior of the estimator. This operation doesn't actually evaluate
+            // data until we read the data below.
             var normalizeTransform = normalize.Fit(data);
             var transformedData = normalizeTransform.Transform(data);
             var normalizeFixZeroTransform = normalizeFixZero.Fit(data);
             var fixZeroData = normalizeFixZeroTransform.Transform(data);
             var column = transformedData.GetColumn<float[]>("Features").ToArray();
             foreach (var row in column)
-                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString("f4"))));
+                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString(
+                    "f4"))));
             // Expected output:
             //  0.6667, 0.6667, 1.0000, 0.0000
             //  1.0000, 1.0000, 0.7500, 0.0000
             //  0.3333, 0.3333, 0.5000, 0.0000
             //  0.0000, 0.0000, 0.0000, 1.0000
 
-            var columnFixZero = fixZeroData.GetColumn<float[]>("Features").ToArray();
+            var columnFixZero = fixZeroData.GetColumn<float[]>("Features")
+                .ToArray();
+
             foreach (var row in columnFixZero)
-                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString("f4"))));
+                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString(
+                    "f4"))));
             // Expected output:
             //  0.5000, 0.5000, 1.0000, 0.0000
             //  1.0000, 1.0000, 0.6667, 0.0000
             //  0.0000, 0.0000, 0.3333, 0.0000
             // -0.5000,-0.5000,-0.3333, 1.0000
 
-            // Let's get transformation parameters. Since we work with only one column we need to pass 0 as parameter for GetNormalizerModelParameters.
-            // If we have multiple columns transformations we need to pass index of InputOutputColumnPair.
-            var transformParams = normalizeTransform.GetNormalizerModelParameters(0) as AffineNormalizerModelParameters<ImmutableArray<float>>;
-            Console.WriteLine($"The 1-index value in resulting array would be produce by:");
-             Console.WriteLine($" y = (x - ({(transformParams.Offset.Length == 0 ? 0 : transformParams.Offset[1])})) * {transformParams.Scale[1]}");
+            // Let's get transformation parameters. Since we work with only one
+            // column we need to pass 0 as parameter for
+            // GetNormalizerModelParameters. If we have multiple columns
+            // transformations we need to pass index of InputOutputColumnPair.
+            var transformParams = normalizeTransform.GetNormalizerModelParameters(0)
+                as AffineNormalizerModelParameters<ImmutableArray<float>>;
+
+            Console.WriteLine($"The 1-index value in resulting array would be " +
+                $"produce by:");
+
+             Console.WriteLine(" y = (x - (" + (transformParams.Offset.Length == 0 ?
+                 0 : transformParams.Offset[1]) + ")) * " + transformParams
+                 .Scale[1]);
             // Expected output:
             //  The 1-index value in resulting array would be produce by: 
             //  y = (x - (-1)) * 0.3333333
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeSupervisedBinning.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeSupervisedBinning.cs
index 63fde50a9e..9441324639 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeSupervisedBinning.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeSupervisedBinning.cs
@@ -12,39 +12,58 @@ public class NormalizeSupervisedBinning
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
             var samples = new List<DataPoint>()
             {
-                new DataPoint(){ Features = new float[4] { 8, 1, 3, 0}, Bin="Bin1" },
-                new DataPoint(){ Features = new float[4] { 6, 2, 2, 1}, Bin="Bin2" },
-                new DataPoint(){ Features = new float[4] { 5, 3, 0, 2}, Bin="Bin2" },
-                new DataPoint(){ Features = new float[4] { 4,-8, 1, 3}, Bin="Bin3" },
-                new DataPoint(){ Features = new float[4] { 2,-5,-1, 4}, Bin="Bin3" }
+                new DataPoint(){ Features = new float[4] { 8, 1, 3, 0},
+                    Bin ="Bin1" },
+
+                new DataPoint(){ Features = new float[4] { 6, 2, 2, 1},
+                    Bin ="Bin2" },
+
+                new DataPoint(){ Features = new float[4] { 5, 3, 0, 2},
+                    Bin ="Bin2" },
+
+                new DataPoint(){ Features = new float[4] { 4,-8, 1, 3},
+                    Bin ="Bin3" },
+
+                new DataPoint(){ Features = new float[4] { 2,-5,-1, 4},
+                    Bin ="Bin3" }
             };
-            // Convert training data to IDataView, the general data type used in ML.NET.
+            // Convert training data to IDataView, the general data type used in
+            // ML.NET.
             var data = mlContext.Data.LoadFromEnumerable(samples);
             // Let's transform "Bin" column from string to key.
-            data = mlContext.Transforms.Conversion.MapValueToKey("Bin").Fit(data).Transform(data);
-            // NormalizeSupervisedBinning normalizes the data by constructing bins based on correlation with the label column and produce output based on 
-            // to which bin original value belong.
-            var normalize = mlContext.Transforms.NormalizeSupervisedBinning("Features", labelColumnName: "Bin", mininimumExamplesPerBin: 1, fixZero: false);
-
-            // NormalizeSupervisedBinning normalizes the data by constructing bins based on correlation with the label column and produce output based on 
-            // to which bin original value belong but make sure zero values would remain zero after normalization.
-            // Helps preserve sparsity.
-            var normalizeFixZero = mlContext.Transforms.NormalizeSupervisedBinning("Features", labelColumnName: "Bin", mininimumExamplesPerBin: 1, fixZero: true);
-
-            // Now we can transform the data and look at the output to confirm the behavior of the estimator.
-            // This operation doesn't actually evaluate data until we read the data below.
+            data = mlContext.Transforms.Conversion.MapValueToKey("Bin").Fit(data)
+                .Transform(data);
+            // NormalizeSupervisedBinning normalizes the data by constructing bins
+            // based on correlation with the label column and produce output based
+            // on to which bin original value belong.
+            var normalize = mlContext.Transforms.NormalizeSupervisedBinning(
+                "Features", labelColumnName: "Bin", mininimumExamplesPerBin: 1,
+                fixZero: false);
+
+            // NormalizeSupervisedBinning normalizes the data by constructing bins
+            // based on correlation with the label column and produce output based
+            // on to which bin original value belong but make sure zero values would
+            // remain zero after normalization. Helps preserve sparsity.
+            var normalizeFixZero = mlContext.Transforms.NormalizeSupervisedBinning(
+                "Features", labelColumnName: "Bin", mininimumExamplesPerBin: 1,
+                fixZero: true);
+
+            // Now we can transform the data and look at the output to confirm the
+            // behavior of the estimator. This operation doesn't actually evaluate
+            // data until we read the data below.
             var normalizeTransform = normalize.Fit(data);
             var transformedData = normalizeTransform.Transform(data);
             var normalizeFixZeroTransform = normalizeFixZero.Fit(data);
             var fixZeroData = normalizeFixZeroTransform.Transform(data);
             var column = transformedData.GetColumn<float[]>("Features").ToArray();
             foreach (var row in column)
-                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString("f4"))));
+                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString(
+                    "f4"))));
             // Expected output:
             //  1.0000, 0.5000, 1.0000, 0.0000
             //  0.5000, 1.0000, 0.0000, 0.5000
@@ -52,9 +71,12 @@ public static void Example()
             //  0.0000, 0.0000, 0.0000, 1.0000
             //  0.0000, 0.0000, 0.0000, 1.0000
 
-            var columnFixZero = fixZeroData.GetColumn<float[]>("Features").ToArray();
+            var columnFixZero = fixZeroData.GetColumn<float[]>("Features")
+                .ToArray();
+
             foreach (var row in columnFixZero)
-                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString("f4"))));
+                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString(
+                    "f4"))));
             // Expected output:
             //  1.0000, 0.0000, 1.0000, 0.0000
             //  0.5000, 0.5000, 0.0000, 0.5000
@@ -62,24 +84,48 @@ public static void Example()
             //  0.0000,-0.5000, 0.0000, 1.0000
             //  0.0000,-0.5000, 0.0000, 1.0000
 
-            // Let's get transformation parameters. Since we work with only one column we need to pass 0 as parameter for GetNormalizerModelParameters.
-            // If we have multiple columns transformations we need to pass index of InputOutputColumnPair.
-            var transformParams = normalizeTransform.GetNormalizerModelParameters(0) as BinNormalizerModelParameters<ImmutableArray<float>>;
-            Console.WriteLine($"The 1-index value in resulting array would be produce by:");
-            Console.WriteLine($"y = (Index(x) / {transformParams.Density[0]}) - {(transformParams.Offset.Length == 0 ? 0 : transformParams.Offset[0])}");
-            Console.WriteLine("Where Index(x) is the index of the bin to which x belongs");
-            Console.WriteLine($"Bins upper borders are: {string.Join(" ", transformParams.UpperBounds[0])}");
+            // Let's get transformation parameters. Since we work with only one
+            // column we need to pass 0 as parameter for
+            // GetNormalizerModelParameters.
+            // If we have multiple columns transformations we need to pass index of
+            // InputOutputColumnPair.
+            var transformParams = normalizeTransform.GetNormalizerModelParameters(0)
+                as BinNormalizerModelParameters<ImmutableArray<float>>;
+
+            Console.WriteLine($"The 1-index value in resulting array would be " +
+                $"produce by:");
+
+            Console.WriteLine("y = (Index(x) / " + transformParams.Density[0] + 
+                ") - " + (transformParams.Offset.Length == 0 ? 0 : transformParams
+                .Offset[0]));
+
+            Console.WriteLine("Where Index(x) is the index of the bin to which " +
+                "x belongs");
+
+            Console.WriteLine("Bins upper borders are: " + string.Join(" ",
+                transformParams.UpperBounds[0]));
             // Expected output:
             //  The 1-index value in resulting array would be produce by:
             //  y = (Index(x) / 2) - 0
             //  Where Index(x) is the index of the bin to which x belongs
             //  Bins upper bounds are: 4.5 7 ∞
 
-            var fixZeroParams = normalizeFixZeroTransform.GetNormalizerModelParameters(0) as BinNormalizerModelParameters<ImmutableArray<float>>;
-            Console.WriteLine($"The 1-index value in resulting array would be produce by:");
-            Console.WriteLine($" y = (Index(x) / {fixZeroParams.Density[1]}) - {(fixZeroParams.Offset.Length == 0 ? 0 : fixZeroParams.Offset[1])}");
-            Console.WriteLine("Where Index(x) is the index of the bin to which x belongs");
-            Console.WriteLine($"Bins upper borders are: {string.Join(" ", fixZeroParams.UpperBounds[1])}");
+            var fixZeroParams = normalizeFixZeroTransform
+                .GetNormalizerModelParameters(0) as BinNormalizerModelParameters<
+                ImmutableArray<float>>;
+
+            Console.WriteLine($"The 1-index value in resulting array would be " +
+                $"produce by:");
+
+            Console.WriteLine(" y = (Index(x) / " + fixZeroParams.Density[1] +
+                ") - " + (fixZeroParams.Offset.Length == 0 ? 0 : fixZeroParams
+                .Offset[1]));
+
+            Console.WriteLine("Where Index(x) is the index of the bin to which x " +
+                "belongs");
+
+            Console.WriteLine("Bins upper borders are: " + string.Join(" ",
+                fixZeroParams.UpperBounds[1]));
             // Expected output:
             //  The 1-index value in resulting array would be produce by:
             //  y = (Index(x) / 2) - 0.5
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ReplaceMissingValues.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ReplaceMissingValues.cs
index d30c6812ce..83ba740a0d 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ReplaceMissingValues.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ReplaceMissingValues.cs
@@ -11,35 +11,46 @@ class ReplaceMissingValues
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
             // Get a small dataset as an IEnumerable and convert it to an IDataView.
             var samples = new List<DataPoint>()
             {
-                new DataPoint(){ Features = new float[3] {float.PositiveInfinity, 1, 0} },
+                new DataPoint(){ Features = new float[3] {float.PositiveInfinity, 1,
+                    0 } },
+
                 new DataPoint(){ Features = new float[3] {0, float.NaN, 1} },
                 new DataPoint(){ Features = new float[3] {-1, 2, -3} },
                 new DataPoint(){ Features = new float[3] {-1, float.NaN, -3} },
             };
             var data = mlContext.Data.LoadFromEnumerable(samples);
 
-            // Here we use the default replacement mode, which replaces the value with the default value for its type.
-            var defaultPipeline = mlContext.Transforms.ReplaceMissingValues("MissingReplaced", "Features",
-                MissingValueReplacingEstimator.ReplacementMode.DefaultValue);
+            // Here we use the default replacement mode, which replaces the value
+            // with the default value for its type.
+            var defaultPipeline = mlContext.Transforms.ReplaceMissingValues(
+                "MissingReplaced", "Features", MissingValueReplacingEstimator
+                .ReplacementMode.DefaultValue);
 
-            // Now we can transform the data and look at the output to confirm the behavior of the estimator.
-            // This operation doesn't actually evaluate data until we read the data below.
+            // Now we can transform the data and look at the output to confirm the
+            // behavior of the estimator. This operation doesn't actually evaluate
+            // data until we read the data below.
             var defaultTransformer = defaultPipeline.Fit(data);
             var defaultTransformedData = defaultTransformer.Transform(data);
 
-            // We can extract the newly created column as an IEnumerable of SampleDataTransformed, the class we define below.
-            var defaultRowEnumerable = mlContext.Data.CreateEnumerable<SampleDataTransformed>(defaultTransformedData, reuseRowObject: false);
+            // We can extract the newly created column as an IEnumerable of
+            // SampleDataTransformed, the class we define below.
+            var defaultRowEnumerable = mlContext.Data.CreateEnumerable<
+                SampleDataTransformed>(defaultTransformedData, reuseRowObject:
+                false);
 
-            // And finally, we can write out the rows of the dataset, looking at the columns of interest.
+            // And finally, we can write out the rows of the dataset, looking at the
+            // columns of interest.
             foreach (var row in defaultRowEnumerable)
-                Console.WriteLine($"Features: [{string.Join(", ", row.Features)}]\t MissingReplaced: [{string.Join(", ", row.MissingReplaced)}]");
+                Console.WriteLine("Features: [" + string.Join(", ", row.Features) +
+                    "]\t MissingReplaced: [" + string.Join(", ", row
+                    .MissingReplaced) + "]");
 
             // Expected output:
             // Features: [∞, 1, 0]      MissingReplaced: [∞, 1, 0]
@@ -47,21 +58,29 @@ public static void Example()
             // Features: [-1, 2, -3]    MissingReplaced: [-1, 2, -3]
             // Features: [-1, NaN, -3]  MissingReplaced: [-1, 0, -3]
 
-            // Here we use the mean replacement mode, which replaces the value with the mean of the non values that were not missing.
-            var meanPipeline = mlContext.Transforms.ReplaceMissingValues("MissingReplaced", "Features",
-                MissingValueReplacingEstimator.ReplacementMode.Mean);
+            // Here we use the mean replacement mode, which replaces the value with
+            // the mean of the non values that were not missing.
+            var meanPipeline = mlContext.Transforms.ReplaceMissingValues(
+                "MissingReplaced", "Features", MissingValueReplacingEstimator
+                .ReplacementMode.Mean);
 
-            // Now we can transform the data and look at the output to confirm the behavior of the estimator.
-            // This operation doesn't actually evaluate data until we read the data below.
+            // Now we can transform the data and look at the output to confirm the
+            // behavior of the estimator. This operation doesn't actually evaluate
+            // data until we read the data below.
             var meanTransformer = meanPipeline.Fit(data);
             var meanTransformedData = meanTransformer.Transform(data);
 
-            // We can extract the newly created column as an IEnumerable of SampleDataTransformed, the class we define below.
-            var meanRowEnumerable = mlContext.Data.CreateEnumerable<SampleDataTransformed>(meanTransformedData, reuseRowObject: false);
+            // We can extract the newly created column as an IEnumerable of
+            // SampleDataTransformed, the class we define below.
+            var meanRowEnumerable = mlContext.Data.CreateEnumerable<
+                SampleDataTransformed>(meanTransformedData, reuseRowObject: false);
 
-            // And finally, we can write out the rows of the dataset, looking at the columns of interest.
+            // And finally, we can write out the rows of the dataset, looking at the
+            // columns of interest.
             foreach (var row in meanRowEnumerable)
-                Console.WriteLine($"Features: [{string.Join(", ", row.Features)}]\t MissingReplaced: [{string.Join(", ", row.MissingReplaced)}]");
+                Console.WriteLine("Features: [" + string.Join(", ", row.Features) +
+                    "]\t MissingReplaced: [" + string.Join(", ", row
+                    .MissingReplaced) + "]");
 
             // Expected output:
             // Features: [∞, 1, 0]      MissingReplaced: [∞, 1, 0]
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ReplaceMissingValuesMultiColumn.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ReplaceMissingValuesMultiColumn.cs
index aa5d1acf5b..2f17e5334b 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ReplaceMissingValuesMultiColumn.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ReplaceMissingValuesMultiColumn.cs
@@ -10,39 +10,55 @@ class ReplaceMissingValuesMultiColumn
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
             // Get a small dataset as an IEnumerable and convert it to an IDataView.
             var samples = new List<DataPoint>()
             {
-                new DataPoint(){ Features1 = new float[3] {1, 1, 0}, Features2 = new float[2] {1, 1} },
-                new DataPoint(){ Features1 = new float[3] {0, float.NaN, 1}, Features2 = new float[2] {0, 1} },
-                new DataPoint(){ Features1 = new float[3] {-1, float.NaN, -3}, Features2 = new float[2] {-1, float.NaN} },
-                new DataPoint(){ Features1 = new float[3] {-1, 6, -3}, Features2 = new float[2] {0, float.PositiveInfinity} },
+                new DataPoint(){ Features1 = new float[3] {1, 1, 0}, Features2 =
+                    new float[2] {1, 1} },
+
+                new DataPoint(){ Features1 = new float[3] {0, float.NaN, 1},
+                    Features2 = new float[2] {0, 1} },
+
+                new DataPoint(){ Features1 = new float[3] {-1, float.NaN, -3},
+                    Features2 = new float[2] {-1, float.NaN} },
+
+                new DataPoint(){ Features1 = new float[3] {-1, 6, -3}, Features2 =
+                    new float[2] {0, float.PositiveInfinity} },
             };
             var data = mlContext.Data.LoadFromEnumerable(samples);
 
-            // Here we use the default replacement mode, which replaces the value with the default value for its type.
+            // Here we use the default replacement mode, which replaces the value
+            // with the default value for its type.
             var defaultPipeline = mlContext.Transforms.ReplaceMissingValues(new[] {
                 new InputOutputColumnPair("MissingReplaced1", "Features1"),
                 new InputOutputColumnPair("MissingReplaced2", "Features2")
             },
             MissingValueReplacingEstimator.ReplacementMode.DefaultValue);
 
-            // Now we can transform the data and look at the output to confirm the behavior of the estimator.
-            // This operation doesn't actually evaluate data until we read the data below.
+            // Now we can transform the data and look at the output to confirm the
+            // behavior of the estimator. This operation doesn't actually evaluate
+            // data until we read the data below.
             var defaultTransformer = defaultPipeline.Fit(data);
             var defaultTransformedData = defaultTransformer.Transform(data);
 
-            // We can extract the newly created column as an IEnumerable of SampleDataTransformed, the class we define below.
-            var defaultRowEnumerable = mlContext.Data.CreateEnumerable<SampleDataTransformed>(defaultTransformedData, reuseRowObject: false);
+            // We can extract the newly created column as an IEnumerable of
+            // SampleDataTransformed, the class we define below.
+            var defaultRowEnumerable = mlContext.Data.CreateEnumerable<
+                SampleDataTransformed>(defaultTransformedData, reuseRowObject:
+                false);
 
-            // And finally, we can write out the rows of the dataset, looking at the columns of interest.
+            // And finally, we can write out the rows of the dataset, looking at the
+            // columns of interest.
             foreach (var row in defaultRowEnumerable)
-                Console.WriteLine($"Features1: [{string.Join(", ", row.Features1)}]\t MissingReplaced1: [{string.Join(", ", row.MissingReplaced1)}]\t " +
-                    $"Features2: [{ string.Join(", ", row.Features2)}]\t MissingReplaced2: [{string.Join(", ", row.MissingReplaced2)}]");
+                Console.WriteLine("Features1: [" + string.Join(", ", row
+                    .Features1) + "]\t MissingReplaced1: [" + string.Join(", ", row
+                    .MissingReplaced1) + "]\t Features2: [" + string.Join(", ", row
+                    .Features2) + "]\t MissingReplaced2: [" + string.Join(", ", row
+                    .MissingReplaced2) + "]");
 
             // Expected output:
             // Features1: [1, 1, 0]     MissingReplaced1: [1, 1, 0]     Features2: [1, 1]       MissingReplaced2: [1, 1]
@@ -50,25 +66,34 @@ public static void Example()
             // Features1: [-1, NaN, -3]         MissingReplaced1: [-1, 0, -3]   Features2: [-1, NaN]    MissingReplaced2: [-1, 0]
             // Features1: [-1, 6, -3]   MissingReplaced1: [-1, 6, -3]   Features2: [0, ∞]       MissingReplaced2: [0, ∞]
 
-            // Here we use the mean replacement mode, which replaces the value with the mean of the non values that were not missing.
+            // Here we use the mean replacement mode, which replaces the value with
+            // the mean of the non values that were not missing.
             var meanPipeline = mlContext.Transforms.ReplaceMissingValues(new[] {
                 new InputOutputColumnPair("MissingReplaced1", "Features1"),
                 new InputOutputColumnPair("MissingReplaced2", "Features2")
             },
             MissingValueReplacingEstimator.ReplacementMode.Mean);
 
-            // Now we can transform the data and look at the output to confirm the behavior of the estimator.
-            // This operation doesn't actually evaluate data until we read the data below.
+            // Now we can transform the data and look at the output to confirm the
+            // behavior of the estimator.
+            // This operation doesn't actually evaluate data until we read the data
+            // below.
             var meanTransformer = meanPipeline.Fit(data);
             var meanTransformedData = meanTransformer.Transform(data);
 
-            // We can extract the newly created column as an IEnumerable of SampleDataTransformed, the class we define below.
-            var meanRowEnumerable = mlContext.Data.CreateEnumerable<SampleDataTransformed>(meanTransformedData, reuseRowObject: false);
+            // We can extract the newly created column as an IEnumerable of
+            // SampleDataTransformed, the class we define below.
+            var meanRowEnumerable = mlContext.Data.CreateEnumerable<
+                SampleDataTransformed>(meanTransformedData, reuseRowObject: false);
 
-            // And finally, we can write out the rows of the dataset, looking at the columns of interest.
+            // And finally, we can write out the rows of the dataset, looking at the
+            // columns of interest.
             foreach (var row in meanRowEnumerable)
-                Console.WriteLine($"Features1: [{string.Join(", ", row.Features1)}]\t MissingReplaced1: [{string.Join(", ", row.MissingReplaced1)}]\t " +
-                    $"Features2: [{ string.Join(", ", row.Features2)}]\t MissingReplaced2: [{string.Join(", ", row.MissingReplaced2)}]");
+                Console.WriteLine("Features1: [" + string.Join(", ", row
+                    .Features1) + "]\t MissingReplaced1: [" + string.Join(", ", row
+                    .MissingReplaced1) + "]\t Features2: [" + string.Join(", ", row
+                    .Features2) + "]\t MissingReplaced2: [" + string.Join(", ", row
+                    .MissingReplaced2) + "]");
 
             // Expected output:
             // Features1: [1, 1, 0]     MissingReplaced1: [1, 1, 0]     Features2: [1, 1]       MissingReplaced2: [1, 1]
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/SelectColumns.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/SelectColumns.cs
index 796617d58f..22efcdc085 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/SelectColumns.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/SelectColumns.cs
@@ -8,19 +8,30 @@ public static class SelectColumns
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
             // Create a small dataset as an IEnumerable.
             var samples = new List<InputData>()
             {
-                new InputData(){ Age = 21, Gender = "Male", Education = "BS", ExtraColumn = 1 },
-                new InputData(){ Age = 23, Gender = "Female", Education = "MBA", ExtraColumn = 2 },
-                new InputData(){ Age = 28, Gender = "Male", Education = "PhD", ExtraColumn = 3 },
-                new InputData(){ Age = 22, Gender = "Male", Education = "BS", ExtraColumn = 4 },
-                new InputData(){ Age = 23, Gender = "Female", Education = "MS", ExtraColumn = 5 },
-                new InputData(){ Age = 27, Gender = "Female", Education = "PhD", ExtraColumn = 6 },
+                new InputData(){ Age = 21, Gender = "Male", Education = "BS",
+                    ExtraColumn = 1 },
+
+                new InputData(){ Age = 23, Gender = "Female", Education = "MBA",
+                    ExtraColumn = 2 },
+
+                new InputData(){ Age = 28, Gender = "Male", Education = "PhD",
+                    ExtraColumn = 3 },
+
+                new InputData(){ Age = 22, Gender = "Male", Education = "BS",
+                    ExtraColumn = 4 },
+
+                new InputData(){ Age = 23, Gender = "Female", Education = "MS",
+                    ExtraColumn = 5 },
+
+                new InputData(){ Age = 27, Gender = "Female", Education = "PhD",
+                    ExtraColumn = 6 },
             };
 
             // Convert training data to IDataView.
@@ -29,22 +40,29 @@ public static void Example()
             // Select a subset of columns to keep.
             var pipeline = mlContext.Transforms.SelectColumns("Age", "Education");
 
-            // Now we can transform the data and look at the output to confirm the behavior of SelectColumns.
-            // Don't forget that this operation doesn't actually evaluate data until we read the data below,
-            // as transformations are lazy in ML.NET.
+            // Now we can transform the data and look at the output to confirm the
+            // behavior of SelectColumns. Don't forget that this operation doesn't
+            // actually evaluate data until we read the data below, as
+            // transformations are lazy in ML.NET.
             var transformedData = pipeline.Fit(dataview).Transform(dataview);
 
             // Print the number of columns in the schema
-            Console.WriteLine($"There are {transformedData.Schema.Count} columns in the dataset.");
+            Console.WriteLine($"There are {transformedData.Schema.Count} columns" +
+                $" in the dataset.");
 
             // Expected output:
             //  There are 2 columns in the dataset.
 
-            // We can extract the newly created column as an IEnumerable of TransformedData, the class we define below.
-            var rowEnumerable = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, reuseRowObject: false);
+            // We can extract the newly created column as an IEnumerable of
+            // TransformedData, the class we define below.
+            var rowEnumerable = mlContext.Data.CreateEnumerable<TransformedData>(
+                transformedData, reuseRowObject: false);
+
+            // And finally, we can write out the rows of the dataset, looking at the
+            // columns of interest.
+            Console.WriteLine($"Age and Educations columns obtained " +
+                $"post-transformation.");
 
-            // And finally, we can write out the rows of the dataset, looking at the columns of interest.
-            Console.WriteLine($"Age and Educations columns obtained post-transformation.");
             foreach (var row in rowEnumerable)
                 Console.WriteLine($"Age: {row.Age} Education: {row.Education}");
 
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/BinaryClassificationFeaturization.ttinclude b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/BinaryClassificationFeaturization.ttinclude
index ec5507c3a7..b223b29df4 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/BinaryClassificationFeaturization.ttinclude
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/BinaryClassificationFeaturization.ttinclude
@@ -13,21 +13,25 @@ namespace Samples.Dynamic.Transforms.TreeFeaturization
     {<#=Comments#>
         public static void Example()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
-            // as a catalog of available operations and as the source of randomness.
-            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
 
             // Create a list of data points to be transformed.
             var dataPoints = GenerateRandomDataPoints(100).ToList();
 
-            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            // Convert the list of data points to an IDataView object, which is
+            // consumable by ML.NET API.
             var dataView = mlContext.Data.LoadFromEnumerable(dataPoints);
 <# if (CacheData) { #>
 
-            // ML.NET doesn't cache data set by default. Therefore, if one reads a data set from a file and accesses it many times,
-            // it can be slow due to expensive featurization and disk operations. When the considered data can fit into memory,
-            // a solution is to cache the data in memory. Caching is especially helpful when working with iterative algorithms 
+            // ML.NET doesn't cache data set by default. Therefore, if one reads a
+            // data set from a file and accesses it many times, it can be slow due
+            // to expensive featurization and disk operations. When the considered
+            // data can fit into memory, a solution is to cache the data in memory.
+            // Caching is especially helpful when working with iterative algorithms 
             // which needs many data passes.
             dataView = mlContext.Data.Cache(dataView);
 <# } #>
@@ -39,14 +43,16 @@ namespace Samples.Dynamic.Transforms.TreeFeaturization
             string leavesColumnName = nameof(TransformedDataPoint.Leaves);
             string pathsColumnName = nameof(TransformedDataPoint.Paths);
 
-            // Define the configuration of the trainer used to train a tree-based model.
+            // Define the configuration of the trainer used to train a tree-based
+            // model.
             var trainerOptions = new <#=TrainerOptions#>;
 
             // Define the tree-based featurizer's configuration.
             var options = new <#=Options#>;
 
             // Define the featurizer.
-            var pipeline = mlContext.Transforms.<#=Trainer#>(options);
+            var pipeline = mlContext.Transforms.<#=Trainer#>(
+                options);
 
             // Train the model.
             var model = pipeline.Fit(dataView);
@@ -54,24 +60,35 @@ namespace Samples.Dynamic.Transforms.TreeFeaturization
             // Apply the trained transformer to the considered data set.
             var transformed = model.Transform(dataView);
 
-            // Convert IDataView object to a list. Each element in the resulted list corresponds to a row in the IDataView.
-            var transformedDataPoints = mlContext.Data.CreateEnumerable<TransformedDataPoint>(transformed, false).ToList();
+            // Convert IDataView object to a list. Each element in the resulted list
+            // corresponds to a row in the IDataView.
+            var transformedDataPoints = mlContext.Data.CreateEnumerable<
+                TransformedDataPoint>(transformed, false).ToList();
 
             // Print out the transformation of the first 3 data points.
             for (int i = 0; i < 3; ++i)
             {
                 var dataPoint = dataPoints[i];
                 var transformedDataPoint = transformedDataPoints[i];
-                Console.WriteLine($"The original feature vector [{String.Join(",", dataPoint.Features)}] is transformed to three different tree-based feature vectors:");
-                Console.WriteLine($"  Trees' output values: [{String.Join(",", transformedDataPoint.Trees)}].");
-                Console.WriteLine($"  Leave IDs' 0-1 representation: [{String.Join(",", transformedDataPoint.Leaves)}].");
-                Console.WriteLine($"  Paths IDs' 0-1 representation: [{String.Join(",", transformedDataPoint.Paths)}].");
+                Console.WriteLine("The original feature vector [" + String.Join(
+                    ",", dataPoint.Features) + "] is transformed to three " +
+                    "different tree-based feature vectors:");
+
+                Console.WriteLine("  Trees' output values: [" + String.Join(",",
+                    transformedDataPoint.Trees) + "].");
+
+                Console.WriteLine("  Leave IDs' 0-1 representation: [" + String
+                    .Join(",", transformedDataPoint.Leaves) + "].");
+
+                Console.WriteLine("  Paths IDs' 0-1 representation: [" + String
+                    .Join(",", transformedDataPoint.Paths) + "].");
             }
 
             <#=ExpectedOutput#>
         }
 
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed=0)
         {
             var random = new Random(seed);
             float randomFloat() => (float)random.NextDouble();
@@ -82,13 +99,16 @@ namespace Samples.Dynamic.Transforms.TreeFeaturization
                 {
                     Label = label,
                     // Create random features that are correlated with the label.
-                    // For data points with false label, the feature values are slightly increased by adding a constant.
-                    Features = Enumerable.Repeat(label, 3).Select(x => x ? randomFloat() : randomFloat() + <#=DataSepValue#>).ToArray()
+                    // For data points with false label, the feature values are
+                    // slightly increased by adding a constant.
+                    Features = Enumerable.Repeat(label, 3).Select(x => x ?
+                    randomFloat() : randomFloat() + <#=DataSepValue#>).ToArray()
                 };
             }
         }
 
-        // Example with label and 3 feature values. A data set is a collection of such examples.
+        // Example with label and 3 feature values. A data set is a collection of
+        // such examples.
         private class DataPoint
         {
             public bool Label { get; set; }
@@ -103,7 +123,8 @@ namespace Samples.Dynamic.Transforms.TreeFeaturization
             public float[] Trees { get; set; }
             // The 0-1 encoding of leaves the input feature vector falls into.
             public float[] Leaves { get; set; }
-            // The 0-1 encoding of paths the input feature vector reaches the leaves.
+            // The 0-1 encoding of paths the input feature vector reaches the
+            // leaves.
             public float[] Paths { get; set; }
         }
     }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastForestBinaryFeaturizationWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastForestBinaryFeaturizationWithOptions.cs
index 6c9b100fa4..9ca0b87a85 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastForestBinaryFeaturizationWithOptions.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastForestBinaryFeaturizationWithOptions.cs
@@ -13,20 +13,24 @@ public static class FastForestBinaryFeaturizationWithOptions
         // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
         public static void Example()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
-            // as a catalog of available operations and as the source of randomness.
-            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
 
             // Create a list of data points to be transformed.
             var dataPoints = GenerateRandomDataPoints(100).ToList();
 
-            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            // Convert the list of data points to an IDataView object, which is
+            // consumable by ML.NET API.
             var dataView = mlContext.Data.LoadFromEnumerable(dataPoints);
 
-            // ML.NET doesn't cache data set by default. Therefore, if one reads a data set from a file and accesses it many times,
-            // it can be slow due to expensive featurization and disk operations. When the considered data can fit into memory,
-            // a solution is to cache the data in memory. Caching is especially helpful when working with iterative algorithms 
+            // ML.NET doesn't cache data set by default. Therefore, if one reads a
+            // data set from a file and accesses it many times, it can be slow due
+            // to expensive featurization and disk operations. When the considered
+            // data can fit into memory, a solution is to cache the data in memory.
+            // Caching is especially helpful when working with iterative algorithms 
             // which needs many data passes.
             dataView = mlContext.Data.Cache(dataView);
 
@@ -37,7 +41,8 @@ public static void Example()
             string leavesColumnName = nameof(TransformedDataPoint.Leaves);
             string pathsColumnName = nameof(TransformedDataPoint.Paths);
 
-            // Define the configuration of the trainer used to train a tree-based model.
+            // Define the configuration of the trainer used to train a tree-based
+            // model.
             var trainerOptions = new FastForestBinaryTrainer.Options
             {
                 // Create a simpler model by penalizing usage of new features.
@@ -63,7 +68,8 @@ public static void Example()
             };
 
             // Define the featurizer.
-            var pipeline = mlContext.Transforms.FeaturizeByFastForestBinary(options);
+            var pipeline = mlContext.Transforms.FeaturizeByFastForestBinary(
+                options);
 
             // Train the model.
             var model = pipeline.Fit(dataView);
@@ -71,36 +77,50 @@ public static void Example()
             // Apply the trained transformer to the considered data set.
             var transformed = model.Transform(dataView);
 
-            // Convert IDataView object to a list. Each element in the resulted list corresponds to a row in the IDataView.
-            var transformedDataPoints = mlContext.Data.CreateEnumerable<TransformedDataPoint>(transformed, false).ToList();
+            // Convert IDataView object to a list. Each element in the resulted list
+            // corresponds to a row in the IDataView.
+            var transformedDataPoints = mlContext.Data.CreateEnumerable<
+                TransformedDataPoint>(transformed, false).ToList();
 
             // Print out the transformation of the first 3 data points.
             for (int i = 0; i < 3; ++i)
             {
                 var dataPoint = dataPoints[i];
                 var transformedDataPoint = transformedDataPoints[i];
-                Console.WriteLine($"The original feature vector [{String.Join(",", dataPoint.Features)}] is transformed to three different tree-based feature vectors:");
-                Console.WriteLine($"  Trees' output values: [{String.Join(",", transformedDataPoint.Trees)}].");
-                Console.WriteLine($"  Leave IDs' 0-1 representation: [{String.Join(",", transformedDataPoint.Leaves)}].");
-                Console.WriteLine($"  Paths IDs' 0-1 representation: [{String.Join(",", transformedDataPoint.Paths)}].");
+                Console.WriteLine("The original feature vector [" + String.Join(
+                    ",", dataPoint.Features) + "] is transformed to three " +
+                    "different tree-based feature vectors:");
+
+                Console.WriteLine("  Trees' output values: [" + String.Join(",",
+                    transformedDataPoint.Trees) + "].");
+
+                Console.WriteLine("  Leave IDs' 0-1 representation: [" + String
+                    .Join(",", transformedDataPoint.Leaves) + "].");
+
+                Console.WriteLine("  Paths IDs' 0-1 representation: [" + String
+                    .Join(",", transformedDataPoint.Paths) + "].");
             }
 
             // Expected output:
-            //  The original feature vector [0.8173254,0.7680227,0.5581612] is transformed to three different tree-based feature vectors:
+            //  The original feature vector [0.8173254,0.7680227,0.5581612] is
+            //  transformed to three different tree-based feature vectors:
             //    Trees' output values: [0.1111111,0.8823529].
             //    Leave IDs' 0-1 representation: [0,0,0,0,1,0,0,0,0,1,0].
             //    Paths IDs' 0-1 representation: [1,1,1,1,1,1,0,1,0].
-            //  The original feature vector [0.5888848,0.9360271,0.4721779] is transformed to three different tree-based feature vectors:
+            //  The original feature vector [0.5888848,0.9360271,0.4721779] is
+            //  transformed to three different tree-based feature vectors:
             //    Trees' output values: [0.4545455,0.8].
             //    Leave IDs' 0-1 representation: [0,0,0,1,0,0,0,0,0,0,1].
             //    Paths IDs' 0-1 representation: [1,1,1,1,0,1,0,1,1].
-            //  The original feature vector [0.2737045,0.2919063,0.4673147] is transformed to three different tree-based feature vectors:
+            //  The original feature vector [0.2737045,0.2919063,0.4673147] is
+            //  transformed to three different tree-based feature vectors:
             //    Trees' output values: [0.4545455,0.1111111].
             //    Leave IDs' 0-1 representation: [0,0,0,1,0,0,1,0,0,0,0].
             //    Paths IDs' 0-1 representation: [1,1,1,1,0,1,0,1,1].
         }
 
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed=0)
         {
             var random = new Random(seed);
             float randomFloat() => (float)random.NextDouble();
@@ -111,13 +131,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                 {
                     Label = label,
                     // Create random features that are correlated with the label.
-                    // For data points with false label, the feature values are slightly increased by adding a constant.
-                    Features = Enumerable.Repeat(label, 3).Select(x => x ? randomFloat() : randomFloat() + 0.03f).ToArray()
+                    // For data points with false label, the feature values are
+                    // slightly increased by adding a constant.
+                    Features = Enumerable.Repeat(label, 3).Select(x => x ?
+                    randomFloat() : randomFloat() + 0.03f).ToArray()
                 };
             }
         }
 
-        // Example with label and 3 feature values. A data set is a collection of such examples.
+        // Example with label and 3 feature values. A data set is a collection of
+        // such examples.
         private class DataPoint
         {
             public bool Label { get; set; }
@@ -132,7 +155,8 @@ private class TransformedDataPoint : DataPoint
             public float[] Trees { get; set; }
             // The 0-1 encoding of leaves the input feature vector falls into.
             public float[] Leaves { get; set; }
-            // The 0-1 encoding of paths the input feature vector reaches the leaves.
+            // The 0-1 encoding of paths the input feature vector reaches the
+            // leaves.
             public float[] Paths { get; set; }
         }
     }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastForestBinaryFeaturizationWithOptions.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastForestBinaryFeaturizationWithOptions.tt
index a2640ea95e..e88e911a26 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastForestBinaryFeaturizationWithOptions.tt
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastForestBinaryFeaturizationWithOptions.tt
@@ -24,7 +24,8 @@ string TrainerOptions = @"FastForestBinaryTrainer.Options
                 LabelColumnName = labelColumnName
             }";
 
-string Options = @"FastForestBinaryFeaturizationEstimator.Options
+string Options = @"FastForestBinaryFeaturizationEstimator
+    .Options
             {
                 InputColumnName = featureColumnName,
                 TreesColumnName = treesColumnName,
@@ -34,15 +35,18 @@ string Options = @"FastForestBinaryFeaturizationEstimator.Options
             }";
 
 string ExpectedOutput = @"// Expected output:
-            //  The original feature vector [0.8173254,0.7680227,0.5581612] is transformed to three different tree-based feature vectors:
+            //  The original feature vector [0.8173254,0.7680227,0.5581612] is
+            //  transformed to three different tree-based feature vectors:
             //    Trees' output values: [0.1111111,0.8823529].
             //    Leave IDs' 0-1 representation: [0,0,0,0,1,0,0,0,0,1,0].
             //    Paths IDs' 0-1 representation: [1,1,1,1,1,1,0,1,0].
-            //  The original feature vector [0.5888848,0.9360271,0.4721779] is transformed to three different tree-based feature vectors:
+            //  The original feature vector [0.5888848,0.9360271,0.4721779] is
+            //  transformed to three different tree-based feature vectors:
             //    Trees' output values: [0.4545455,0.8].
             //    Leave IDs' 0-1 representation: [0,0,0,1,0,0,0,0,0,0,1].
             //    Paths IDs' 0-1 representation: [1,1,1,1,0,1,0,1,1].
-            //  The original feature vector [0.2737045,0.2919063,0.4673147] is transformed to three different tree-based feature vectors:
+            //  The original feature vector [0.2737045,0.2919063,0.4673147] is
+            //  transformed to three different tree-based feature vectors:
             //    Trees' output values: [0.4545455,0.1111111].
             //    Leave IDs' 0-1 representation: [0,0,0,1,0,0,1,0,0,0,0].
             //    Paths IDs' 0-1 representation: [1,1,1,1,0,1,0,1,1].";
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastForestRegressionFeaturizationWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastForestRegressionFeaturizationWithOptions.cs
index ca6c5a27c8..cc107d35fd 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastForestRegressionFeaturizationWithOptions.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastForestRegressionFeaturizationWithOptions.cs
@@ -13,20 +13,24 @@ public static class FastForestRegressionFeaturizationWithOptions
         // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>. 
         public static void Example()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
-            // as a catalog of available operations and as the source of randomness.
-            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
 
             // Create a list of training data points.
             var dataPoints = GenerateRandomDataPoints(100).ToList();
 
-            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            // Convert the list of data points to an IDataView object, which is
+            // consumable by ML.NET API.
             var dataView = mlContext.Data.LoadFromEnumerable(dataPoints);
 
-            // ML.NET doesn't cache data set by default. Therefore, if one reads a data set from a file and accesses it many times,
-            // it can be slow due to expensive featurization and disk operations. When the considered data can fit into memory,
-            // a solution is to cache the data in memory. Caching is especially helpful when working with iterative algorithms 
+            // ML.NET doesn't cache data set by default. Therefore, if one reads a
+            // data set from a file and accesses it many times, it can be slow due
+            // to expensive featurization and disk operations. When the considered
+            // data can fit into memory, a solution is to cache the data in memory.
+            // Caching is especially helpful when working with iterative algorithms 
             // which needs many data passes.
             dataView = mlContext.Data.Cache(dataView);
 
@@ -37,7 +41,8 @@ public static void Example()
             string leavesColumnName = nameof(TransformedDataPoint.Leaves);
             string pathsColumnName = nameof(TransformedDataPoint.Paths);
 
-            // Define the configuration of the trainer used to train a tree-based model.
+            // Define the configuration of the trainer used to train a tree-based
+            // model.
             var trainerOptions = new FastForestRegressionTrainer.Options
             {
                 // Only use 80% of features to reduce over-fitting.
@@ -63,44 +68,60 @@ public static void Example()
             };
 
             // Define the featurizer.
-            var pipeline = mlContext.Transforms.FeaturizeByFastForestRegression(options);
+            var pipeline = mlContext.Transforms.FeaturizeByFastForestRegression(
+                options);
 
             // Train the model.
             var model = pipeline.Fit(dataView);
 
-            // Create testing data. Use different random seed to make it different from training data.
+            // Create testing data. Use different random seed to make it different
+            // from training data.
             var transformed = model.Transform(dataView);
 
-            // Convert IDataView object to a list. Each element in the resulted list corresponds to a row in the IDataView.
-            var transformedDataPoints = mlContext.Data.CreateEnumerable<TransformedDataPoint>(transformed, false).ToList();
+            // Convert IDataView object to a list. Each element in the resulted list
+            // corresponds to a row in the IDataView.
+            var transformedDataPoints = mlContext.Data.CreateEnumerable<
+                TransformedDataPoint>(transformed, false).ToList();
 
             // Print out the transformation of the first 3 data points.
             for (int i = 0; i < 3; ++i)
             {
                 var dataPoint = dataPoints[i];
                 var transformedDataPoint = transformedDataPoints[i];
-                Console.WriteLine($"The original feature vector [{String.Join(",", dataPoint.Features)}] is transformed to three different tree-based feature vectors:");
-                Console.WriteLine($"  Trees' output values: [{String.Join(",", transformedDataPoint.Trees)}].");
-                Console.WriteLine($"  Leave IDs' 0-1 representation: [{String.Join(",", transformedDataPoint.Leaves)}].");
-                Console.WriteLine($"  Paths IDs' 0-1 representation: [{String.Join(",", transformedDataPoint.Paths)}].");
+                Console.WriteLine("The original feature vector [" + String.Join(",",
+                    dataPoint.Features) + "] is transformed to three different " +
+                    "tree-based feature vectors:");
+
+                Console.WriteLine("  Trees' output values: [" + String.Join(",",
+                    transformedDataPoint.Trees) + "].");
+
+                Console.WriteLine("  Leave IDs' 0-1 representation: [" + String
+                    .Join(",", transformedDataPoint.Leaves) + "].");
+
+                Console.WriteLine("  Paths IDs' 0-1 representation: [" + String
+                    .Join(",", transformedDataPoint.Paths) + "].");
             }
 
             // Expected output:
-            //   The original feature vector[1.543569, 1.494266, 1.284405] is transformed to three different tree - based feature vectors:
+            //   The original feature vector[1.543569, 1.494266, 1.284405] is
+            //   transformed to three different tree - based feature vectors:
             //     Trees' output values: [0.7291142,0.7825329,0.8764582].
             //     Leave IDs' 0-1 representation: [0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0].
             //     Paths IDs' 0-1 representation: [1,0,0,0,1,1,1,0,0,1,1,1,0,1].
-            //   The original feature vector[0.764918, 1.11206, 0.648211] is transformed to three different tree - based feature vectors:
+            //   The original feature vector[0.764918, 1.11206, 0.648211] is
+            //   transformed to three different tree - based feature vectors:
             //     Trees' output values: [0.3802337,0.584159,0.5648927].
             //     Leave IDs' 0-1 representation: [0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0].
             //     Paths IDs' 0-1 representation: [1,1,1,0,0,1,1,0,0,0,1,1,0,0].
-            //   The original feature vector[1.251254, 1.269456, 1.444864] is transformed to three different tree - based feature vectors:
+            //   The original feature vector[1.251254, 1.269456, 1.444864] is
+            //   transformed to three different tree - based feature vectors:
             //     Trees' output values: [0.7591804,0.7825329,0.7443035].
             //     Leave IDs' 0-1 representation: [0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1].
             //     Paths IDs' 0-1 representation: [1,0,0,0,1,1,1,0,0,1,1,1,0,1].
         }
 
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed=0)
         {
             var random = new Random(seed);
             for (int i = 0; i < count; i++)
@@ -110,12 +131,14 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                 {
                     Label = label,
                     // Create random features that are correlated with the label.
-                    Features = Enumerable.Repeat(label, 3).Select(x => x + (float)random.NextDouble()).ToArray()
+                    Features = Enumerable.Repeat(label, 3).Select(x => x + 
+                        (float)random.NextDouble()).ToArray()
                 };
             }
         }
 
-        // Example with label and 50 feature values. A data set is a collection of such examples.
+        // Example with label and 50 feature values. A data set is a collection of
+        // such examples.
         private class DataPoint
         {
             public float Label { get; set; }
@@ -130,7 +153,8 @@ private class TransformedDataPoint : DataPoint
             public float[] Trees { get; set; }
             // The 0-1 encoding of leaves the input feature vector falls into.
             public float[] Leaves { get; set; }
-            // The 0-1 encoding of paths the input feature vector reaches the leaves.
+            // The 0-1 encoding of paths the input feature vector reaches the
+            // leaves.
             public float[] Paths { get; set; }
         }
     }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastForestRegressionFeaturizationWithOptions.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastForestRegressionFeaturizationWithOptions.tt
index 1d949629d4..a73542dacb 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastForestRegressionFeaturizationWithOptions.tt
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastForestRegressionFeaturizationWithOptions.tt
@@ -32,15 +32,18 @@ string Options = @"FastForestRegressionFeaturizationEstimator.Options
             }";
 
 string ExpectedOutput = @"// Expected output:
-            //   The original feature vector[1.543569, 1.494266, 1.284405] is transformed to three different tree - based feature vectors:
+            //   The original feature vector[1.543569, 1.494266, 1.284405] is
+            //   transformed to three different tree - based feature vectors:
             //     Trees' output values: [0.7291142,0.7825329,0.8764582].
             //     Leave IDs' 0-1 representation: [0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0].
             //     Paths IDs' 0-1 representation: [1,0,0,0,1,1,1,0,0,1,1,1,0,1].
-            //   The original feature vector[0.764918, 1.11206, 0.648211] is transformed to three different tree - based feature vectors:
+            //   The original feature vector[0.764918, 1.11206, 0.648211] is
+            //   transformed to three different tree - based feature vectors:
             //     Trees' output values: [0.3802337,0.584159,0.5648927].
             //     Leave IDs' 0-1 representation: [0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0].
             //     Paths IDs' 0-1 representation: [1,1,1,0,0,1,1,0,0,0,1,1,0,0].
-            //   The original feature vector[1.251254, 1.269456, 1.444864] is transformed to three different tree - based feature vectors:
+            //   The original feature vector[1.251254, 1.269456, 1.444864] is
+            //   transformed to three different tree - based feature vectors:
             //     Trees' output values: [0.7591804,0.7825329,0.7443035].
             //     Leave IDs' 0-1 representation: [0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1].
             //     Paths IDs' 0-1 representation: [1,0,0,0,1,1,1,0,0,1,1,1,0,1].";
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeBinaryFeaturizationWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeBinaryFeaturizationWithOptions.cs
index c8c52e1490..521e04e23b 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeBinaryFeaturizationWithOptions.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeBinaryFeaturizationWithOptions.cs
@@ -13,20 +13,24 @@ public static class FastTreeBinaryFeaturizationWithOptions
         // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
         public static void Example()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
-            // as a catalog of available operations and as the source of randomness.
-            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
 
             // Create a list of data points to be transformed.
             var dataPoints = GenerateRandomDataPoints(100).ToList();
 
-            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            // Convert the list of data points to an IDataView object, which is
+            // consumable by ML.NET API.
             var dataView = mlContext.Data.LoadFromEnumerable(dataPoints);
 
-            // ML.NET doesn't cache data set by default. Therefore, if one reads a data set from a file and accesses it many times,
-            // it can be slow due to expensive featurization and disk operations. When the considered data can fit into memory,
-            // a solution is to cache the data in memory. Caching is especially helpful when working with iterative algorithms 
+            // ML.NET doesn't cache data set by default. Therefore, if one reads a
+            // data set from a file and accesses it many times, it can be slow due
+            // to expensive featurization and disk operations. When the considered
+            // data can fit into memory, a solution is to cache the data in memory.
+            // Caching is especially helpful when working with iterative algorithms 
             // which needs many data passes.
             dataView = mlContext.Data.Cache(dataView);
 
@@ -37,7 +41,8 @@ public static void Example()
             string leavesColumnName = nameof(TransformedDataPoint.Leaves);
             string pathsColumnName = nameof(TransformedDataPoint.Paths);
 
-            // Define the configuration of the trainer used to train a tree-based model.
+            // Define the configuration of the trainer used to train a tree-based
+            // model.
             var trainerOptions = new FastTreeBinaryTrainer.Options
             {
                 // Use L2Norm for early stopping.
@@ -65,7 +70,8 @@ public static void Example()
             };
 
             // Define the featurizer.
-            var pipeline = mlContext.Transforms.FeaturizeByFastTreeBinary(options);
+            var pipeline = mlContext.Transforms.FeaturizeByFastTreeBinary(
+                options);
 
             // Train the model.
             var model = pipeline.Fit(dataView);
@@ -73,36 +79,50 @@ public static void Example()
             // Apply the trained transformer to the considered data set.
             var transformed = model.Transform(dataView);
 
-            // Convert IDataView object to a list. Each element in the resulted list corresponds to a row in the IDataView.
-            var transformedDataPoints = mlContext.Data.CreateEnumerable<TransformedDataPoint>(transformed, false).ToList();
+            // Convert IDataView object to a list. Each element in the resulted list
+            // corresponds to a row in the IDataView.
+            var transformedDataPoints = mlContext.Data.CreateEnumerable<
+                TransformedDataPoint>(transformed, false).ToList();
 
             // Print out the transformation of the first 3 data points.
             for (int i = 0; i < 3; ++i)
             {
                 var dataPoint = dataPoints[i];
                 var transformedDataPoint = transformedDataPoints[i];
-                Console.WriteLine($"The original feature vector [{String.Join(",", dataPoint.Features)}] is transformed to three different tree-based feature vectors:");
-                Console.WriteLine($"  Trees' output values: [{String.Join(",", transformedDataPoint.Trees)}].");
-                Console.WriteLine($"  Leave IDs' 0-1 representation: [{String.Join(",", transformedDataPoint.Leaves)}].");
-                Console.WriteLine($"  Paths IDs' 0-1 representation: [{String.Join(",", transformedDataPoint.Paths)}].");
+                Console.WriteLine("The original feature vector [" + String.Join(
+                    ",", dataPoint.Features) + "] is transformed to three " +
+                    "different tree-based feature vectors:");
+
+                Console.WriteLine("  Trees' output values: [" + String.Join(",",
+                    transformedDataPoint.Trees) + "].");
+
+                Console.WriteLine("  Leave IDs' 0-1 representation: [" + String
+                    .Join(",", transformedDataPoint.Leaves) + "].");
+
+                Console.WriteLine("  Paths IDs' 0-1 representation: [" + String
+                    .Join(",", transformedDataPoint.Paths) + "].");
             }
 
             // Expected output:
-            //   The original feature vector [0.8173254,0.7680227,0.5581612] is transformed to three different tree-based feature vectors:
+            //   The original feature vector [0.8173254,0.7680227,0.5581612] is
+            //   transformed to three different tree-based feature vectors:
             //     Trees' output values: [0.5714286,0.4636412,0.535588].
             //     Leave IDs' 0-1 representation: [0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1].
             //     Paths IDs' 0-1 representation: [1,0,0,1,1,1,0,1,0,1,1,1,1,1,1].
-            //   The original feature vector [0.5888848,0.9360271,0.4721779] is transformed to three different tree-based feature vectors:
+            //   The original feature vector [0.5888848,0.9360271,0.4721779] is
+            //   transformed to three different tree-based feature vectors:
             //     Trees' output values: [0.2352941,-0.1382389,0.535588].
             //     Leave IDs' 0-1 representation: [0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1].
             //     Paths IDs' 0-1 representation: [1,0,0,1,1,1,0,1,0,1,1,1,1,1,1].
-            //   The original feature vector [0.2737045,0.2919063,0.4673147] is transformed to three different tree-based feature vectors:
+            //   The original feature vector [0.2737045,0.2919063,0.4673147] is
+            //   transformed to three different tree-based feature vectors:
             //     Trees' output values: [0.2352941,-0.1382389,-0.2184284].
             //     Leave IDs' 0-1 representation: [0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0].
             //     Paths IDs' 0-1 representation: [1,0,0,1,1,1,0,1,0,1,1,1,0,0,0].
         }
 
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed=0)
         {
             var random = new Random(seed);
             float randomFloat() => (float)random.NextDouble();
@@ -113,13 +133,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                 {
                     Label = label,
                     // Create random features that are correlated with the label.
-                    // For data points with false label, the feature values are slightly increased by adding a constant.
-                    Features = Enumerable.Repeat(label, 3).Select(x => x ? randomFloat() : randomFloat() + 0.03f).ToArray()
+                    // For data points with false label, the feature values are
+                    // slightly increased by adding a constant.
+                    Features = Enumerable.Repeat(label, 3).Select(x => x ?
+                    randomFloat() : randomFloat() + 0.03f).ToArray()
                 };
             }
         }
 
-        // Example with label and 3 feature values. A data set is a collection of such examples.
+        // Example with label and 3 feature values. A data set is a collection of
+        // such examples.
         private class DataPoint
         {
             public bool Label { get; set; }
@@ -134,7 +157,8 @@ private class TransformedDataPoint : DataPoint
             public float[] Trees { get; set; }
             // The 0-1 encoding of leaves the input feature vector falls into.
             public float[] Leaves { get; set; }
-            // The 0-1 encoding of paths the input feature vector reaches the leaves.
+            // The 0-1 encoding of paths the input feature vector reaches the
+            // leaves.
             public float[] Paths { get; set; }
         }
     }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeBinaryFeaturizationWithOptions.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeBinaryFeaturizationWithOptions.tt
index ec055986d5..857365347f 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeBinaryFeaturizationWithOptions.tt
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeBinaryFeaturizationWithOptions.tt
@@ -42,15 +42,18 @@ string ExpectedOutputPerInstance= @"// Expected output:
             //   Label: False, Prediction: False";
 
 string ExpectedOutput = @"// Expected output:
-            //   The original feature vector [0.8173254,0.7680227,0.5581612] is transformed to three different tree-based feature vectors:
+            //   The original feature vector [0.8173254,0.7680227,0.5581612] is
+            //   transformed to three different tree-based feature vectors:
             //     Trees' output values: [0.5714286,0.4636412,0.535588].
             //     Leave IDs' 0-1 representation: [0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1].
             //     Paths IDs' 0-1 representation: [1,0,0,1,1,1,0,1,0,1,1,1,1,1,1].
-            //   The original feature vector [0.5888848,0.9360271,0.4721779] is transformed to three different tree-based feature vectors:
+            //   The original feature vector [0.5888848,0.9360271,0.4721779] is
+            //   transformed to three different tree-based feature vectors:
             //     Trees' output values: [0.2352941,-0.1382389,0.535588].
             //     Leave IDs' 0-1 representation: [0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1].
             //     Paths IDs' 0-1 representation: [1,0,0,1,1,1,0,1,0,1,1,1,1,1,1].
-            //   The original feature vector [0.2737045,0.2919063,0.4673147] is transformed to three different tree-based feature vectors:
+            //   The original feature vector [0.2737045,0.2919063,0.4673147] is
+            //   transformed to three different tree-based feature vectors:
             //     Trees' output values: [0.2352941,-0.1382389,-0.2184284].
             //     Leave IDs' 0-1 representation: [0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0].
             //     Paths IDs' 0-1 representation: [1,0,0,1,1,1,0,1,0,1,1,1,0,0,0].";
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeRankingFeaturizationWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeRankingFeaturizationWithOptions.cs
index 9e525643d3..e11df0eb70 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeRankingFeaturizationWithOptions.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeRankingFeaturizationWithOptions.cs
@@ -13,20 +13,24 @@ public static class FastTreeRankingFeaturizationWithOptions
         // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
         public static void Example()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
-            // as a catalog of available operations and as the source of randomness.
-            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
 
             // Create a list of training data points.
             var dataPoints = GenerateRandomDataPoints(100).ToList();
 
-            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            // Convert the list of data points to an IDataView object, which is
+            // consumable by ML.NET API.
             var dataView = mlContext.Data.LoadFromEnumerable(dataPoints);
 
-            // ML.NET doesn't cache data set by default. Therefore, if one reads a data set from a file and accesses it many times,
-            // it can be slow due to expensive featurization and disk operations. When the considered data can fit into memory,
-            // a solution is to cache the data in memory. Caching is especially helpful when working with iterative algorithms 
+            // ML.NET doesn't cache data set by default. Therefore, if one reads a
+            // data set from a file and accesses it many times, it can be slow due
+            // to expensive featurization and disk operations. When the considered
+            // data can fit into memory, a solution is to cache the data in memory.
+            // Caching is especially helpful when working with iterative algorithms
             // which needs many data passes.
             dataView = mlContext.Data.Cache(dataView);
 
@@ -37,7 +41,8 @@ public static void Example()
             string leavesColumnName = nameof(TransformedDataPoint.Leaves);
             string pathsColumnName = nameof(TransformedDataPoint.Paths);
 
-            // Define the configuration of the trainer used to train a tree-based model.
+            // Define the configuration of the trainer used to train a tree-based
+            // model.
             var trainerOptions = new FastTreeRankingTrainer.Options
             {
                 // Reduce the number of trees to 3.
@@ -61,7 +66,8 @@ public static void Example()
             };
 
             // Define the featurizer.
-            var pipeline = mlContext.Transforms.FeaturizeByFastTreeRanking(options);
+            var pipeline = mlContext.Transforms.FeaturizeByFastTreeRanking(
+                options);
 
             // Train the model.
             var model = pipeline.Fit(dataView);
@@ -69,36 +75,50 @@ public static void Example()
             // Apply the trained transformer to the considered data set.
             var transformed = model.Transform(dataView);
 
-            // Convert IDataView object to a list. Each element in the resulted list corresponds to a row in the IDataView.
-            var transformedDataPoints = mlContext.Data.CreateEnumerable<TransformedDataPoint>(transformed, false).ToList();
+            // Convert IDataView object to a list. Each element in the resulted list
+            // corresponds to a row in the IDataView.
+            var transformedDataPoints = mlContext.Data.CreateEnumerable<
+                TransformedDataPoint>(transformed, false).ToList();
 
             // Print out the transformation of the first 3 data points.
             for (int i = 0; i < 3; ++i)
             {
                 var dataPoint = dataPoints[i];
                 var transformedDataPoint = transformedDataPoints[i];
-                Console.WriteLine($"The original feature vector [{String.Join(",", dataPoint.Features)}] is transformed to three different tree-based feature vectors:");
-                Console.WriteLine($"  Trees' output values: [{String.Join(",", transformedDataPoint.Trees)}].");
-                Console.WriteLine($"  Leave IDs' 0-1 representation: [{String.Join(",", transformedDataPoint.Leaves)}].");
-                Console.WriteLine($"  Paths IDs' 0-1 representation: [{String.Join(",", transformedDataPoint.Paths)}].");
+                Console.WriteLine("The original feature vector [" + String.Join(",",
+                    dataPoint.Features) + "] is transformed to three different " +
+                    "tree-based feature vectors:");
+
+                Console.WriteLine("  Trees' output values: [" + String.Join(",",
+                    transformedDataPoint.Trees) + "].");
+
+                Console.WriteLine("  Leave IDs' 0-1 representation: [" + String
+                    .Join(",", transformedDataPoint.Leaves) + "].");
+
+                Console.WriteLine("  Paths IDs' 0-1 representation: [" + String
+                    .Join(",", transformedDataPoint.Paths) + "].");
             }
 
             // Expected output:
-            //   The original feature vector [1.117325,1.068023,0.8581612] is transformed to three different tree-based feature vectors:
+            //   The original feature vector [1.117325,1.068023,0.8581612] is
+            //   transformed to three different tree-based feature vectors:
             //     Trees' output values: [0.4095458,0.2061437,0.2364294].
             //     Leave IDs' 0-1 representation: [0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1].
             //     Paths IDs' 0-1 representation: [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1].
-            //   The original feature vector [0.6588848,1.006027,0.5421779] is transformed to three different tree-based feature vectors:
+            //   The original feature vector [0.6588848,1.006027,0.5421779] is
+            //   transformed to three different tree-based feature vectors:
             //     Trees' output values: [0.2543825,-0.06570309,-0.1456212].
             //     Leave IDs' 0-1 representation: [0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0].
             //     Paths IDs' 0-1 representation: [1,1,1,1,1,1,1,1,1,1,1,1,1,1,0].
-            //   The original feature vector [0.6737045,0.6919063,0.8673147] is transformed to three different tree-based feature vectors:
+            //   The original feature vector [0.6737045,0.6919063,0.8673147] is
+            //   transformed to three different tree-based feature vectors:
             //     Trees' output values: [0.2543825,-0.06570309,0.01300209].
             //     Leave IDs' 0-1 representation: [0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0].
             //     Paths IDs' 0-1 representation: [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1].
         }
 
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0, int groupSize = 10)
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed = 0, int groupSize = 10)
         {
             var random = new Random(seed);
             float randomFloat() => (float)random.NextDouble();
@@ -110,13 +130,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                     Label = (uint)label,
                     GroupId = (uint)(i / groupSize),
                     // Create random features that are correlated with the label.
-                    // For data points with larger labels, the feature values are slightly increased by adding a constant.
-                    Features = Enumerable.Repeat(label, 3).Select(x => randomFloat() + x * 0.1f).ToArray()
+                    // For data points with larger labels, the feature values are
+                    // slightly increased by adding a constant.
+                    Features = Enumerable.Repeat(label, 3).Select(x => randomFloat()
+                        + x * 0.1f).ToArray()
                 };
             }
         }
 
-        // Example with label, groupId, and 3 feature values. A data set is a collection of such examples.
+        // Example with label, groupId, and 3 feature values. A data set is a
+        // collection of such examples.
         private class DataPoint
         {
             [KeyType(5)]
@@ -134,7 +157,8 @@ private class TransformedDataPoint : DataPoint
             public float[] Trees { get; set; }
             // The 0-1 encoding of leaves the input feature vector falls into.
             public float[] Leaves { get; set; }
-            // The 0-1 encoding of paths the input feature vector reaches the leaves.
+            // The 0-1 encoding of paths the input feature vector reaches the
+            // leaves.
             public float[] Paths { get; set; }
         }
     }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeRankingFeaturizationWithOptions.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeRankingFeaturizationWithOptions.tt
index 8be69bf2df..5c1fc30d7d 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeRankingFeaturizationWithOptions.tt
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeRankingFeaturizationWithOptions.tt
@@ -32,15 +32,18 @@ string Comments= @"
         // <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>.";
 
 string ExpectedOutput = @"// Expected output:
-            //   The original feature vector [1.117325,1.068023,0.8581612] is transformed to three different tree-based feature vectors:
+            //   The original feature vector [1.117325,1.068023,0.8581612] is
+            //   transformed to three different tree-based feature vectors:
             //     Trees' output values: [0.4095458,0.2061437,0.2364294].
             //     Leave IDs' 0-1 representation: [0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1].
             //     Paths IDs' 0-1 representation: [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1].
-            //   The original feature vector [0.6588848,1.006027,0.5421779] is transformed to three different tree-based feature vectors:
+            //   The original feature vector [0.6588848,1.006027,0.5421779] is
+            //   transformed to three different tree-based feature vectors:
             //     Trees' output values: [0.2543825,-0.06570309,-0.1456212].
             //     Leave IDs' 0-1 representation: [0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0].
             //     Paths IDs' 0-1 representation: [1,1,1,1,1,1,1,1,1,1,1,1,1,1,0].
-            //   The original feature vector [0.6737045,0.6919063,0.8673147] is transformed to three different tree-based feature vectors:
+            //   The original feature vector [0.6737045,0.6919063,0.8673147] is
+            //   transformed to three different tree-based feature vectors:
             //     Trees' output values: [0.2543825,-0.06570309,0.01300209].
             //     Leave IDs' 0-1 representation: [0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0].
             //     Paths IDs' 0-1 representation: [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1].";
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeRegressionFeaturizationWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeRegressionFeaturizationWithOptions.cs
index c8660e8127..407bb2e22f 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeRegressionFeaturizationWithOptions.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeRegressionFeaturizationWithOptions.cs
@@ -13,20 +13,24 @@ public static class FastTreeRegressionFeaturizationWithOptions
         // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>. 
         public static void Example()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
-            // as a catalog of available operations and as the source of randomness.
-            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
 
             // Create a list of training data points.
             var dataPoints = GenerateRandomDataPoints(100).ToList();
 
-            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            // Convert the list of data points to an IDataView object, which is
+            // consumable by ML.NET API.
             var dataView = mlContext.Data.LoadFromEnumerable(dataPoints);
 
-            // ML.NET doesn't cache data set by default. Therefore, if one reads a data set from a file and accesses it many times,
-            // it can be slow due to expensive featurization and disk operations. When the considered data can fit into memory,
-            // a solution is to cache the data in memory. Caching is especially helpful when working with iterative algorithms 
+            // ML.NET doesn't cache data set by default. Therefore, if one reads a
+            // data set from a file and accesses it many times, it can be slow due
+            // to expensive featurization and disk operations. When the considered
+            // data can fit into memory, a solution is to cache the data in memory.
+            // Caching is especially helpful when working with iterative algorithms 
             // which needs many data passes.
             dataView = mlContext.Data.Cache(dataView);
 
@@ -37,7 +41,8 @@ public static void Example()
             string leavesColumnName = nameof(TransformedDataPoint.Leaves);
             string pathsColumnName = nameof(TransformedDataPoint.Paths);
 
-            // Define the configuration of the trainer used to train a tree-based model.
+            // Define the configuration of the trainer used to train a tree-based
+            // model.
             var trainerOptions = new FastTreeRegressionTrainer.Options
             {
                 // Only use 80% of features to reduce over-fitting.
@@ -63,44 +68,60 @@ public static void Example()
             };
 
             // Define the featurizer.
-            var pipeline = mlContext.Transforms.FeaturizeByFastTreeRegression(options);
+            var pipeline = mlContext.Transforms.FeaturizeByFastTreeRegression(
+                options);
 
             // Train the model.
             var model = pipeline.Fit(dataView);
 
-            // Create testing data. Use different random seed to make it different from training data.
+            // Create testing data. Use different random seed to make it different
+            // from training data.
             var transformed = model.Transform(dataView);
 
-            // Convert IDataView object to a list. Each element in the resulted list corresponds to a row in the IDataView.
-            var transformedDataPoints = mlContext.Data.CreateEnumerable<TransformedDataPoint>(transformed, false).ToList();
+            // Convert IDataView object to a list. Each element in the resulted list
+            // corresponds to a row in the IDataView.
+            var transformedDataPoints = mlContext.Data.CreateEnumerable<
+                TransformedDataPoint>(transformed, false).ToList();
 
             // Print out the transformation of the first 3 data points.
             for (int i = 0; i < 3; ++i)
             {
                 var dataPoint = dataPoints[i];
                 var transformedDataPoint = transformedDataPoints[i];
-                Console.WriteLine($"The original feature vector [{String.Join(",", dataPoint.Features)}] is transformed to three different tree-based feature vectors:");
-                Console.WriteLine($"  Trees' output values: [{String.Join(",", transformedDataPoint.Trees)}].");
-                Console.WriteLine($"  Leave IDs' 0-1 representation: [{String.Join(",", transformedDataPoint.Leaves)}].");
-                Console.WriteLine($"  Paths IDs' 0-1 representation: [{String.Join(",", transformedDataPoint.Paths)}].");
+                Console.WriteLine("The original feature vector [" + String.Join(",",
+                    dataPoint.Features) + "] is transformed to three different " +
+                    "tree-based feature vectors:");
+
+                Console.WriteLine("  Trees' output values: [" + String.Join(",",
+                    transformedDataPoint.Trees) + "].");
+
+                Console.WriteLine("  Leave IDs' 0-1 representation: [" + String
+                    .Join(",", transformedDataPoint.Leaves) + "].");
+
+                Console.WriteLine("  Paths IDs' 0-1 representation: [" + String
+                    .Join(",", transformedDataPoint.Paths) + "].");
             }
 
             // Expected output:
-            //   The original feature vector [1.543569,1.494266,1.284405] is transformed to three different tree-based feature vectors:
+            //   The original feature vector [1.543569,1.494266,1.284405] is
+            //   transformed to three different tree-based feature vectors:
             //     Trees' output values: [0.1507567,0.1372715,0.1019326].
             //     Leave IDs' 0-1 representation: [0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0].
             //     Paths IDs' 0-1 representation: [1,0,0,0,0,1,1,1,0,0,1,1,1,1,0].
-            //   The original feature vector [0.764918,1.11206,0.648211] is transformed to three different tree-based feature vectors:
+            //   The original feature vector [0.764918,1.11206,0.648211] is
+            //   transformed to three different tree-based feature vectors:
             //     Trees' output values: [0.07604675,0.08244576,0.03080027].
             //     Leave IDs' 0-1 representation: [0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1].
             //     Paths IDs' 0-1 representation: [1,1,1,0,0,1,1,0,0,0,1,0,0,0,1].
-            //   The original feature vector [1.251254,1.269456,1.444864] is transformed to three different tree-based feature vectors:
+            //   The original feature vector [1.251254,1.269456,1.444864] is
+            //   transformed to three different tree-based feature vectors:
             //     Trees' output values: [0.1507567,0.1090626,0.0731837].
             //     Leave IDs' 0-1 representation: [0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0].
             //     Paths IDs' 0-1 representation: [1,0,0,0,0,1,1,1,0,0,1,1,1,1,0].
         }
 
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed=0)
         {
             var random = new Random(seed);
             for (int i = 0; i < count; i++)
@@ -110,12 +131,14 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                 {
                     Label = label,
                     // Create random features that are correlated with the label.
-                    Features = Enumerable.Repeat(label, 3).Select(x => x + (float)random.NextDouble()).ToArray()
+                    Features = Enumerable.Repeat(label, 3).Select(x => x + 
+                        (float)random.NextDouble()).ToArray()
                 };
             }
         }
 
-        // Example with label and 50 feature values. A data set is a collection of such examples.
+        // Example with label and 50 feature values. A data set is a collection of
+        // such examples.
         private class DataPoint
         {
             public float Label { get; set; }
@@ -130,7 +153,8 @@ private class TransformedDataPoint : DataPoint
             public float[] Trees { get; set; }
             // The 0-1 encoding of leaves the input feature vector falls into.
             public float[] Leaves { get; set; }
-            // The 0-1 encoding of paths the input feature vector reaches the leaves.
+            // The 0-1 encoding of paths the input feature vector reaches the
+            // leaves.
             public float[] Paths { get; set; }
         }
     }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeRegressionFeaturizationWithOptions.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeRegressionFeaturizationWithOptions.tt
index e22153c900..c03bf4e197 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeRegressionFeaturizationWithOptions.tt
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeRegressionFeaturizationWithOptions.tt
@@ -32,15 +32,18 @@ string Options = @"FastTreeRegressionFeaturizationEstimator.Options
             }";
 
 string ExpectedOutput = @"// Expected output:
-            //   The original feature vector [1.543569,1.494266,1.284405] is transformed to three different tree-based feature vectors:
+            //   The original feature vector [1.543569,1.494266,1.284405] is
+            //   transformed to three different tree-based feature vectors:
             //     Trees' output values: [0.1507567,0.1372715,0.1019326].
             //     Leave IDs' 0-1 representation: [0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0].
             //     Paths IDs' 0-1 representation: [1,0,0,0,0,1,1,1,0,0,1,1,1,1,0].
-            //   The original feature vector [0.764918,1.11206,0.648211] is transformed to three different tree-based feature vectors:
+            //   The original feature vector [0.764918,1.11206,0.648211] is
+            //   transformed to three different tree-based feature vectors:
             //     Trees' output values: [0.07604675,0.08244576,0.03080027].
             //     Leave IDs' 0-1 representation: [0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1].
             //     Paths IDs' 0-1 representation: [1,1,1,0,0,1,1,0,0,0,1,0,0,0,1].
-            //   The original feature vector [1.251254,1.269456,1.444864] is transformed to three different tree-based feature vectors:
+            //   The original feature vector [1.251254,1.269456,1.444864] is
+            //   transformed to three different tree-based feature vectors:
             //     Trees' output values: [0.1507567,0.1090626,0.0731837].
             //     Leave IDs' 0-1 representation: [0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0].
             //     Paths IDs' 0-1 representation: [1,0,0,0,0,1,1,1,0,0,1,1,1,1,0].";
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeTweedieFeaturizationWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeTweedieFeaturizationWithOptions.cs
index b6624560a3..085db4eeb7 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeTweedieFeaturizationWithOptions.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeTweedieFeaturizationWithOptions.cs
@@ -13,20 +13,24 @@ public static class FastTreeTweedieFeaturizationWithOptions
         // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>. 
         public static void Example()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
-            // as a catalog of available operations and as the source of randomness.
-            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
 
             // Create a list of training data points.
             var dataPoints = GenerateRandomDataPoints(100).ToList();
 
-            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            // Convert the list of data points to an IDataView object, which is
+            // consumable by ML.NET API.
             var dataView = mlContext.Data.LoadFromEnumerable(dataPoints);
 
-            // ML.NET doesn't cache data set by default. Therefore, if one reads a data set from a file and accesses it many times,
-            // it can be slow due to expensive featurization and disk operations. When the considered data can fit into memory,
-            // a solution is to cache the data in memory. Caching is especially helpful when working with iterative algorithms 
+            // ML.NET doesn't cache data set by default. Therefore, if one reads a
+            // data set from a file and accesses it many times, it can be slow due
+            // to expensive featurization and disk operations. When the considered
+            // data can fit into memory, a solution is to cache the data in memory.
+            // Caching is especially helpful when working with iterative algorithms 
             // which needs many data passes.
             dataView = mlContext.Data.Cache(dataView);
 
@@ -37,7 +41,8 @@ public static void Example()
             string leavesColumnName = nameof(TransformedDataPoint.Leaves);
             string pathsColumnName = nameof(TransformedDataPoint.Paths);
 
-            // Define the configuration of the trainer used to train a tree-based model.
+            // Define the configuration of the trainer used to train a tree-based
+            // model.
             var trainerOptions = new FastTreeTweedieTrainer.Options
             {
                 // Only use 80% of features to reduce over-fitting.
@@ -63,44 +68,60 @@ public static void Example()
             };
 
             // Define the featurizer.
-            var pipeline = mlContext.Transforms.FeaturizeByFastTreeTweedie(options);
+            var pipeline = mlContext.Transforms.FeaturizeByFastTreeTweedie(
+                options);
 
             // Train the model.
             var model = pipeline.Fit(dataView);
 
-            // Create testing data. Use different random seed to make it different from training data.
+            // Create testing data. Use different random seed to make it different
+            // from training data.
             var transformed = model.Transform(dataView);
 
-            // Convert IDataView object to a list. Each element in the resulted list corresponds to a row in the IDataView.
-            var transformedDataPoints = mlContext.Data.CreateEnumerable<TransformedDataPoint>(transformed, false).ToList();
+            // Convert IDataView object to a list. Each element in the resulted list
+            // corresponds to a row in the IDataView.
+            var transformedDataPoints = mlContext.Data.CreateEnumerable<
+                TransformedDataPoint>(transformed, false).ToList();
 
             // Print out the transformation of the first 3 data points.
             for (int i = 0; i < 3; ++i)
             {
                 var dataPoint = dataPoints[i];
                 var transformedDataPoint = transformedDataPoints[i];
-                Console.WriteLine($"The original feature vector [{String.Join(",", dataPoint.Features)}] is transformed to three different tree-based feature vectors:");
-                Console.WriteLine($"  Trees' output values: [{String.Join(",", transformedDataPoint.Trees)}].");
-                Console.WriteLine($"  Leave IDs' 0-1 representation: [{String.Join(",", transformedDataPoint.Leaves)}].");
-                Console.WriteLine($"  Paths IDs' 0-1 representation: [{String.Join(",", transformedDataPoint.Paths)}].");
+                Console.WriteLine("The original feature vector [" + String.Join(",",
+                    dataPoint.Features) + "] is transformed to three different " +
+                    "tree-based feature vectors:");
+
+                Console.WriteLine("  Trees' output values: [" + String.Join(",",
+                    transformedDataPoint.Trees) + "].");
+
+                Console.WriteLine("  Leave IDs' 0-1 representation: [" + String
+                    .Join(",", transformedDataPoint.Leaves) + "].");
+
+                Console.WriteLine("  Paths IDs' 0-1 representation: [" + String
+                    .Join(",", transformedDataPoint.Paths) + "].");
             }
 
             // Expected output:
-            //   The original feature vector [1.543569,1.494266,1.284405] is transformed to three different tree-based feature vectors:
+            //   The original feature vector [1.543569,1.494266,1.284405] is
+            //   transformed to three different tree-based feature vectors:
             //     Trees' output values: [-0.05652997,-0.02312196,-0.01179363].
             //     Leave IDs' 0-1 representation: [0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0].
             //     Paths IDs' 0-1 representation: [1,0,0,0,0,1,1,0,1,0,1,1,0,0,0].
-            //   The original feature vector [0.764918,1.11206,0.648211] is transformed to three different tree-based feature vectors:
+            //   The original feature vector [0.764918,1.11206,0.648211] is
+            //   transformed to three different tree-based feature vectors:
             //     Trees' output values: [-0.1933938,-0.1042738,-0.2312837].
             //     Leave IDs' 0-1 representation: [0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0].
             //     Paths IDs' 0-1 representation: [1,1,1,0,0,1,1,0,0,0,1,0,0,0,0].
-            //   The original feature vector [1.251254,1.269456,1.444864] is transformed to three different tree-based feature vectors:
+            //   The original feature vector [1.251254,1.269456,1.444864] is
+            //   transformed to three different tree-based feature vectors:
             //     Trees' output values: [-0.05652997,-0.06082304,-0.04528879].
             //     Leave IDs' 0-1 representation: [0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0].
             //     Paths IDs' 0-1 representation: [1,0,0,0,0,1,1,0,1,0,1,1,1,0,1].
         }
 
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed=0)
         {
             var random = new Random(seed);
             for (int i = 0; i < count; i++)
@@ -110,12 +131,14 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                 {
                     Label = label,
                     // Create random features that are correlated with the label.
-                    Features = Enumerable.Repeat(label, 3).Select(x => x + (float)random.NextDouble()).ToArray()
+                    Features = Enumerable.Repeat(label, 3).Select(x => x + 
+                        (float)random.NextDouble()).ToArray()
                 };
             }
         }
 
-        // Example with label and 50 feature values. A data set is a collection of such examples.
+        // Example with label and 50 feature values. A data set is a collection of
+        // such examples.
         private class DataPoint
         {
             public float Label { get; set; }
@@ -130,7 +153,8 @@ private class TransformedDataPoint : DataPoint
             public float[] Trees { get; set; }
             // The 0-1 encoding of leaves the input feature vector falls into.
             public float[] Leaves { get; set; }
-            // The 0-1 encoding of paths the input feature vector reaches the leaves.
+            // The 0-1 encoding of paths the input feature vector reaches the
+            // leaves.
             public float[] Paths { get; set; }
         }
     }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeTweedieFeaturizationWithOptions.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeTweedieFeaturizationWithOptions.tt
index a075887d1e..b1ea23e2b2 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeTweedieFeaturizationWithOptions.tt
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/FastTreeTweedieFeaturizationWithOptions.tt
@@ -39,15 +39,18 @@ string ExpectedOutputPerInstance= @"// Expected output:
             //   Label: 0.096, Prediction: 0.140";
 
 string ExpectedOutput = @"// Expected output:
-            //   The original feature vector [1.543569,1.494266,1.284405] is transformed to three different tree-based feature vectors:
+            //   The original feature vector [1.543569,1.494266,1.284405] is
+            //   transformed to three different tree-based feature vectors:
             //     Trees' output values: [-0.05652997,-0.02312196,-0.01179363].
             //     Leave IDs' 0-1 representation: [0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0].
             //     Paths IDs' 0-1 representation: [1,0,0,0,0,1,1,0,1,0,1,1,0,0,0].
-            //   The original feature vector [0.764918,1.11206,0.648211] is transformed to three different tree-based feature vectors:
+            //   The original feature vector [0.764918,1.11206,0.648211] is
+            //   transformed to three different tree-based feature vectors:
             //     Trees' output values: [-0.1933938,-0.1042738,-0.2312837].
             //     Leave IDs' 0-1 representation: [0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0].
             //     Paths IDs' 0-1 representation: [1,1,1,0,0,1,1,0,0,0,1,0,0,0,0].
-            //   The original feature vector [1.251254,1.269456,1.444864] is transformed to three different tree-based feature vectors:
+            //   The original feature vector [1.251254,1.269456,1.444864] is
+            //   transformed to three different tree-based feature vectors:
             //     Trees' output values: [-0.05652997,-0.06082304,-0.04528879].
             //     Leave IDs' 0-1 representation: [0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0].
             //     Paths IDs' 0-1 representation: [1,0,0,0,0,1,1,0,1,0,1,1,1,0,1].";
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/PretrainedTreeEnsembleFeaturizationWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/PretrainedTreeEnsembleFeaturizationWithOptions.cs
index f96e595a63..b60c74672d 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/PretrainedTreeEnsembleFeaturizationWithOptions.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/PretrainedTreeEnsembleFeaturizationWithOptions.cs
@@ -13,15 +13,17 @@ public static void Example()
         {
             // Create data set
             int dataPointCount = 200;
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
-            // as a catalog of available operations and as the source of randomness.
-            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
 
             // Create a list of training data points.
             var dataPoints = GenerateRandomDataPoints(dataPointCount).ToList();
 
-            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            // Convert the list of data points to an IDataView object, which is
+            // consumable by ML.NET API.
             var dataView = mlContext.Data.LoadFromEnumerable(dataPoints);
 
             // Define input and output columns of tree-based featurizer.
@@ -31,7 +33,8 @@ public static void Example()
             string leavesColumnName = nameof(TransformedDataPoint.Leaves);
             string pathsColumnName = nameof(TransformedDataPoint.Paths);
 
-            // Define a tree model whose trees will be extracted to construct a tree featurizer.
+            // Define a tree model whose trees will be extracted to construct a tree
+            // featurizer.
             var trainer = mlContext.BinaryClassification.Trainers.FastTree(
                 new FastTreeBinaryTrainer.Options
                 {
@@ -57,36 +60,51 @@ public static void Example()
                 PathsColumnName = pathsColumnName
             };
 
-            // Fit the created featurizer. It doesn't perform actual training because a pretrained model is provided.
-            var treeFeaturizer = mlContext.Transforms.FeaturizeByPretrainTreeEnsemble(options).Fit(dataView);
+            // Fit the created featurizer. It doesn't perform actual training
+            // because a pretrained model is provided.
+            var treeFeaturizer = mlContext.Transforms
+                .FeaturizeByPretrainTreeEnsemble(options).Fit(dataView);
 
             // Apply TreeEnsembleFeaturizer to the input data.
             var transformed = treeFeaturizer.Transform(dataView);
 
-            // Convert IDataView object to a list. Each element in the resulted list corresponds to a row in the IDataView.
-            var transformedDataPoints = mlContext.Data.CreateEnumerable<TransformedDataPoint>(transformed, false).ToList();
+            // Convert IDataView object to a list. Each element in the resulted list
+            // corresponds to a row in the IDataView.
+            var transformedDataPoints = mlContext.Data.CreateEnumerable<
+                TransformedDataPoint>(transformed, false).ToList();
 
             // Print out the transformation of the first 3 data points.
             for (int i = 0; i < 3; ++i)
             {
                 var dataPoint = dataPoints[i];
                 var transformedDataPoint = transformedDataPoints[i];
-                Console.WriteLine($"The original feature vector [{String.Join(",", dataPoint.Features)}] is transformed to three different tree-based feature vectors:");
-                Console.WriteLine($"  Trees' output values: [{String.Join(",", transformedDataPoint.Trees)}].");
-                Console.WriteLine($"  Leave IDs' 0-1 representation: [{String.Join(",", transformedDataPoint.Leaves)}].");
-                Console.WriteLine($"  Paths IDs' 0-1 representation: [{String.Join(",", transformedDataPoint.Paths)}].");
+                Console.WriteLine("The original feature vector [" + String.Join(
+                    ",", dataPoint.Features) + "] is transformed to three " +
+                    "different tree-based feature vectors:");
+
+                Console.WriteLine("  Trees' output values: [" + String.Join(",",
+                    transformedDataPoint.Trees) + "].");
+
+                Console.WriteLine("  Leave IDs' 0-1 representation: [" + String
+                    .Join(",", transformedDataPoint.Leaves) + "].");
+
+                Console.WriteLine("  Paths IDs' 0-1 representation: [" + String
+                    .Join(",", transformedDataPoint.Paths) + "].");
             }
 
             // Expected output:
-            //  The original feature vector[0.8173254, 0.7680227, 0.5581612] is transformed to three different tree - based feature vectors:
+            //  The original feature vector[0.8173254, 0.7680227, 0.5581612] is
+            //  transformed to three different tree - based feature vectors:
             //    Trees' output values: [0.4172185].
             //    Leave IDs' 0-1 representation: [1,0,0,0].
             //    Paths IDs' 0-1 representation: [1,1,1].
-            //  The original feature vector[0.7588848, 1.106027, 0.6421779] is transformed to three different tree - based feature vectors:
+            //  The original feature vector[0.7588848, 1.106027, 0.6421779] is
+            //   transformed to three different tree - based feature vectors:
             //    Trees' output values: [-1].
             //    Leave IDs' 0-1 representation: [0,0,1,0].
             //    Paths IDs' 0-1 representation: [1,1,0].
-            //  The original feature vector[0.2737045, 0.2919063, 0.4673147] is transformed to three different tree - based feature vectors:
+            //  The original feature vector[0.2737045, 0.2919063, 0.4673147] is
+            //   transformed to three different tree - based feature vectors:
             //    Trees' output values: [0.4172185].
             //    Leave IDs' 0-1 representation: [1,0,0,0].
             //    Paths IDs' 0-1 representation: [1,1,1].
@@ -104,12 +122,14 @@ public static void Example()
             //     /    Leaf -4
             //   Leaf -1
             //
-            //   Thus, if a data point reaches Leaf indexed by -1, its 0-1 path representation may be [1,1,1] because that data point
+            //   Thus, if a data point reaches Leaf indexed by -1, its 0-1 path
+            //   representation may be [1,1,1] because that data point
             //   went through all Node 0, Node 1, and Node 2.
 
         }
 
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed=0)
         {
             var random = new Random(seed);
             float randomFloat() => (float)random.NextDouble();
@@ -120,13 +140,16 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                 {
                     Label = label,
                     // Create random features that are correlated with the label.
-                    // For data points with false label, the feature values are slightly increased by adding a constant.
-                    Features = Enumerable.Repeat(label, 3).Select(x => x ? randomFloat() : randomFloat() + 0.2f).ToArray()
+                    // For data points with false label, the feature values are
+                    // slightly increased by adding a constant.
+                    Features = Enumerable.Repeat(label, 3).Select(x => x ?
+                    randomFloat() : randomFloat() + 0.2f).ToArray()
                 };
             }
         }
 
-        // Example with label and 3 feature values. A data set is a collection of such examples.
+        // Example with label and 3 feature values. A data set is a collection of
+        // such examples.
         private class DataPoint
         {
             public bool Label { get; set; }
@@ -137,11 +160,12 @@ private class DataPoint
         // Class used to capture the output of tree-base featurization.
         private class TransformedDataPoint : DataPoint
         {
-		    // The i-th value is the output value of the i-th decision tree.
+            // The i-th value is the output value of the i-th decision tree.
             public float[] Trees { get; set; }
-		    // The 0-1 encoding of leaves the input feature vector falls into.
+            // The 0-1 encoding of leaves the input feature vector falls into.
             public float[] Leaves { get; set; }
-		    // The 0-1 encoding of paths the input feature vector reaches the leaves.
+            // The 0-1 encoding of paths the input feature vector reaches the
+            // leaves.
             public float[] Paths { get; set; }
         }
     }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/RankingFeaturization.ttinclude b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/RankingFeaturization.ttinclude
index 16d6858c91..a10b9d8b96 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/RankingFeaturization.ttinclude
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/RankingFeaturization.ttinclude
@@ -13,21 +13,25 @@ namespace Samples.Dynamic.Transforms.TreeFeaturization
     {<#=Comments#>
         public static void Example()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
-            // as a catalog of available operations and as the source of randomness.
-            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
 
             // Create a list of training data points.
             var dataPoints = GenerateRandomDataPoints(100).ToList();
 
-            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            // Convert the list of data points to an IDataView object, which is
+            // consumable by ML.NET API.
             var dataView = mlContext.Data.LoadFromEnumerable(dataPoints);
 <# if (CacheData) { #>
 
-            // ML.NET doesn't cache data set by default. Therefore, if one reads a data set from a file and accesses it many times,
-            // it can be slow due to expensive featurization and disk operations. When the considered data can fit into memory,
-            // a solution is to cache the data in memory. Caching is especially helpful when working with iterative algorithms 
+            // ML.NET doesn't cache data set by default. Therefore, if one reads a
+            // data set from a file and accesses it many times, it can be slow due
+            // to expensive featurization and disk operations. When the considered
+            // data can fit into memory, a solution is to cache the data in memory.
+            // Caching is especially helpful when working with iterative algorithms
             // which needs many data passes.
             dataView = mlContext.Data.Cache(dataView);
 <# } #>
@@ -39,14 +43,16 @@ namespace Samples.Dynamic.Transforms.TreeFeaturization
             string leavesColumnName = nameof(TransformedDataPoint.Leaves);
             string pathsColumnName = nameof(TransformedDataPoint.Paths);
 
-            // Define the configuration of the trainer used to train a tree-based model.
+            // Define the configuration of the trainer used to train a tree-based
+            // model.
             var trainerOptions = new <#=TrainerOptions#>;
 
             // Define the tree-based featurizer's configuration.
             var options = new <#=Options#>;
 
             // Define the featurizer.
-            var pipeline = mlContext.Transforms.<#=Trainer#>(options);
+            var pipeline = mlContext.Transforms.<#=Trainer#>(
+                options);
 
             // Train the model.
             var model = pipeline.Fit(dataView);
@@ -54,24 +60,35 @@ namespace Samples.Dynamic.Transforms.TreeFeaturization
             // Apply the trained transformer to the considered data set.
             var transformed = model.Transform(dataView);
 
-            // Convert IDataView object to a list. Each element in the resulted list corresponds to a row in the IDataView.
-            var transformedDataPoints = mlContext.Data.CreateEnumerable<TransformedDataPoint>(transformed, false).ToList();
+            // Convert IDataView object to a list. Each element in the resulted list
+            // corresponds to a row in the IDataView.
+            var transformedDataPoints = mlContext.Data.CreateEnumerable<
+                TransformedDataPoint>(transformed, false).ToList();
 
             // Print out the transformation of the first 3 data points.
             for (int i = 0; i < 3; ++i)
             {
                 var dataPoint = dataPoints[i];
                 var transformedDataPoint = transformedDataPoints[i];
-                Console.WriteLine($"The original feature vector [{String.Join(",", dataPoint.Features)}] is transformed to three different tree-based feature vectors:");
-                Console.WriteLine($"  Trees' output values: [{String.Join(",", transformedDataPoint.Trees)}].");
-                Console.WriteLine($"  Leave IDs' 0-1 representation: [{String.Join(",", transformedDataPoint.Leaves)}].");
-                Console.WriteLine($"  Paths IDs' 0-1 representation: [{String.Join(",", transformedDataPoint.Paths)}].");
+                Console.WriteLine("The original feature vector [" + String.Join(",",
+                    dataPoint.Features) + "] is transformed to three different " +
+                    "tree-based feature vectors:");
+
+                Console.WriteLine("  Trees' output values: [" + String.Join(",",
+                    transformedDataPoint.Trees) + "].");
+
+                Console.WriteLine("  Leave IDs' 0-1 representation: [" + String
+                    .Join(",", transformedDataPoint.Leaves) + "].");
+
+                Console.WriteLine("  Paths IDs' 0-1 representation: [" + String
+                    .Join(",", transformedDataPoint.Paths) + "].");
             }
 
             <#=ExpectedOutput#>
         }
 
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0, int groupSize = 10)
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed = 0, int groupSize = 10)
         {
             var random = new Random(seed);
             float randomFloat() => (float)random.NextDouble();
@@ -83,13 +100,16 @@ namespace Samples.Dynamic.Transforms.TreeFeaturization
                     Label = (uint)label,
                     GroupId = (uint)(i / groupSize),
                     // Create random features that are correlated with the label.
-                    // For data points with larger labels, the feature values are slightly increased by adding a constant.
-                    Features = Enumerable.Repeat(label, 3).Select(x => randomFloat() + x * 0.1f).ToArray()
+                    // For data points with larger labels, the feature values are
+                    // slightly increased by adding a constant.
+                    Features = Enumerable.Repeat(label, 3).Select(x => randomFloat()
+                        + x * 0.1f).ToArray()
                 };
             }
         }
 
-        // Example with label, groupId, and 3 feature values. A data set is a collection of such examples.
+        // Example with label, groupId, and 3 feature values. A data set is a
+        // collection of such examples.
         private class DataPoint
         {
             [KeyType(5)]
@@ -107,7 +127,8 @@ namespace Samples.Dynamic.Transforms.TreeFeaturization
             public float[] Trees { get; set; }
             // The 0-1 encoding of leaves the input feature vector falls into.
             public float[] Leaves { get; set; }
-            // The 0-1 encoding of paths the input feature vector reaches the leaves.
+            // The 0-1 encoding of paths the input feature vector reaches the
+            // leaves.
             public float[] Paths { get; set; }
         }
     }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/RegressionFeaturization.ttinclude b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/RegressionFeaturization.ttinclude
index 28ee91ffaf..cb684b8387 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/RegressionFeaturization.ttinclude
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TreeFeaturization/RegressionFeaturization.ttinclude
@@ -16,21 +16,25 @@ namespace Samples.Dynamic.Transforms.TreeFeaturization
 <# } #>
         public static void Example()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
-            // as a catalog of available operations and as the source of randomness.
-            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
 
             // Create a list of training data points.
             var dataPoints = GenerateRandomDataPoints(100).ToList();
 
-            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            // Convert the list of data points to an IDataView object, which is
+            // consumable by ML.NET API.
             var dataView = mlContext.Data.LoadFromEnumerable(dataPoints);
 <# if (CacheData) { #>
 
-            // ML.NET doesn't cache data set by default. Therefore, if one reads a data set from a file and accesses it many times,
-            // it can be slow due to expensive featurization and disk operations. When the considered data can fit into memory,
-            // a solution is to cache the data in memory. Caching is especially helpful when working with iterative algorithms 
+            // ML.NET doesn't cache data set by default. Therefore, if one reads a
+            // data set from a file and accesses it many times, it can be slow due
+            // to expensive featurization and disk operations. When the considered
+            // data can fit into memory, a solution is to cache the data in memory.
+            // Caching is especially helpful when working with iterative algorithms 
             // which needs many data passes.
             dataView = mlContext.Data.Cache(dataView);
 <# } #>
@@ -42,39 +46,53 @@ namespace Samples.Dynamic.Transforms.TreeFeaturization
             string leavesColumnName = nameof(TransformedDataPoint.Leaves);
             string pathsColumnName = nameof(TransformedDataPoint.Paths);
 
-            // Define the configuration of the trainer used to train a tree-based model.
+            // Define the configuration of the trainer used to train a tree-based
+            // model.
             var trainerOptions = new <#=TrainerOptions#>;
 
             // Define the tree-based featurizer's configuration.
             var options = new <#=Options#>;
 
             // Define the featurizer.
-            var pipeline = mlContext.Transforms.<#=Trainer#>(options);
+            var pipeline = mlContext.Transforms.<#=Trainer#>(
+                options);
 
             // Train the model.
             var model = pipeline.Fit(dataView);
 
-            // Create testing data. Use different random seed to make it different from training data.
+            // Create testing data. Use different random seed to make it different
+            // from training data.
             var transformed = model.Transform(dataView);
 
-            // Convert IDataView object to a list. Each element in the resulted list corresponds to a row in the IDataView.
-            var transformedDataPoints = mlContext.Data.CreateEnumerable<TransformedDataPoint>(transformed, false).ToList();
+            // Convert IDataView object to a list. Each element in the resulted list
+            // corresponds to a row in the IDataView.
+            var transformedDataPoints = mlContext.Data.CreateEnumerable<
+                TransformedDataPoint>(transformed, false).ToList();
 
             // Print out the transformation of the first 3 data points.
             for (int i = 0; i < 3; ++i)
             {
                 var dataPoint = dataPoints[i];
                 var transformedDataPoint = transformedDataPoints[i];
-                Console.WriteLine($"The original feature vector [{String.Join(",", dataPoint.Features)}] is transformed to three different tree-based feature vectors:");
-                Console.WriteLine($"  Trees' output values: [{String.Join(",", transformedDataPoint.Trees)}].");
-                Console.WriteLine($"  Leave IDs' 0-1 representation: [{String.Join(",", transformedDataPoint.Leaves)}].");
-                Console.WriteLine($"  Paths IDs' 0-1 representation: [{String.Join(",", transformedDataPoint.Paths)}].");
+                Console.WriteLine("The original feature vector [" + String.Join(",",
+                    dataPoint.Features) + "] is transformed to three different " +
+                    "tree-based feature vectors:");
+
+                Console.WriteLine("  Trees' output values: [" + String.Join(",",
+                    transformedDataPoint.Trees) + "].");
+
+                Console.WriteLine("  Leave IDs' 0-1 representation: [" + String
+                    .Join(",", transformedDataPoint.Leaves) + "].");
+
+                Console.WriteLine("  Paths IDs' 0-1 representation: [" + String
+                    .Join(",", transformedDataPoint.Paths) + "].");
             }
 
             <#=ExpectedOutput#>
         }
 
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed=0)
         {
             var random = new Random(seed);
             for (int i = 0; i < count; i++)
@@ -84,12 +102,14 @@ namespace Samples.Dynamic.Transforms.TreeFeaturization
                 {
                     Label = label,
                     // Create random features that are correlated with the label.
-                    Features = Enumerable.Repeat(label, 3).Select(x => x + (float)random.NextDouble()).ToArray()
+                    Features = Enumerable.Repeat(label, 3).Select(x => x + 
+                        (float)random.NextDouble()).ToArray()
                 };
             }
         }
 
-        // Example with label and 50 feature values. A data set is a collection of such examples.
+        // Example with label and 50 feature values. A data set is a collection of
+        // such examples.
         private class DataPoint
         {
             public float Label { get; set; }
@@ -104,7 +124,8 @@ namespace Samples.Dynamic.Transforms.TreeFeaturization
             public float[] Trees { get; set; }
             // The 0-1 encoding of leaves the input feature vector falls into.
             public float[] Leaves { get; set; }
-            // The 0-1 encoding of paths the input feature vector reaches the leaves.
+            // The 0-1 encoding of paths the input feature vector reaches the
+            // leaves.
             public float[] Paths { get; set; }
         }
     }