diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTree.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTree.cs
index aa0a99e245..9952909c64 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTree.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTree.cs
@@ -8,19 +8,22 @@ namespace Samples.Dynamic.Trainers.Ranking
 {
     public static class FastTree
     {
-        // This example requires installation of additional NuGet package
-        // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
+        // This example requires installation of additional NuGet package for 
+        // Microsoft.ML.FastTree at
+        // https://www.nuget.org/packages/Microsoft.ML.FastTree/
         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(1000);
 
-            // 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 trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
 
             // Define the trainer.
@@ -29,17 +32,21 @@ public static void Example()
             // Train the model.
             var model = pipeline.Fit(trainingData);
 
-            // Create testing data. Use different random seed to make it different from training data.
-            var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
+            // Create testing data. Use different random seed to make it different
+            // from training data.
+            var testData = mlContext.Data.LoadFromEnumerable(
+                GenerateRandomDataPoints(500, seed:123));
 
             // Run the model on test data set.
             var transformedTestData = model.Transform(testData);
 
             // Take the top 5 rows.
-            var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
+            var topTransformedTestData = mlContext.Data.TakeRows(
+                transformedTestData, 5);
 
             // Convert IDataView object to a list.
-            var predictions = mlContext.Data.CreateEnumerable<Prediction>(topTransformedTestData, reuseRowObject: false).ToList();
+            var predictions = mlContext.Data.CreateEnumerable<Prediction>(
+                topTransformedTestData, reuseRowObject: false).ToList();
 
             // Print 5 predictions.
             foreach (var p in predictions)
@@ -61,7 +68,8 @@ public static void Example()
             //   NDCG: @1:0.99, @2:0.98, @3:0.99
         }
 
-        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();
@@ -73,13 +81,16 @@ 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, 50).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, 50).Select(
+       	                x => randomFloat() + x * 0.1f).ToArray()
                 };
             }
         }
 
-        // Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
+        // Example with label, groupId, and 50 feature values. A data set is a
+        // collection of such examples.
         private class DataPoint
         {
             [KeyType(5)]
@@ -102,8 +113,13 @@ private class Prediction
         // Pretty-print RankerMetrics objects.
         public static void PrintMetrics(RankingMetrics metrics)
         {
-            Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
-            Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
+            Console.WriteLine("DCG: " + string.Join(", ",
+                metrics.DiscountedCumulativeGains.Select(
+                (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
+            
+            Console.WriteLine("NDCG: " + string.Join(", ",
+                metrics.NormalizedDiscountedCumulativeGains.Select(
+                (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
         }
     }
 }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTree.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTree.tt
index ac6ee1e854..dfe2abd966 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTree.tt
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTree.tt
@@ -6,8 +6,9 @@ string TrainerOptions = null;
 
 string OptionsInclude = "";
 string Comments= @"
-        // This example requires installation of additional NuGet package
-        // <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>.";
+        // This example requires installation of additional NuGet package for 
+        // Microsoft.ML.FastTree at
+        // https://www.nuget.org/packages/Microsoft.ML.FastTree/";
 
 string ExpectedOutputPerInstance = @"// Expected output:
             //   Label: 5, Score: 13.0154
@@ -19,4 +20,4 @@ string ExpectedOutputPerInstance = @"// Expected output:
 string ExpectedOutput = @"// Expected output:
             //   DCG: @1:41.95, @2:63.33, @3:75.65
             //   NDCG: @1:0.99, @2:0.98, @3:0.99";
-#>
\ No newline at end of file
+#>
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTreeWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTreeWithOptions.cs
index 5683153940..5d575ec160 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTreeWithOptions.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTreeWithOptions.cs
@@ -9,19 +9,22 @@ namespace Samples.Dynamic.Trainers.Ranking
 {
     public static class FastTreeWithOptions
     {
-        // This example requires installation of additional NuGet package
-        // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
+        // This example requires installation of additional NuGet package for 
+        // Microsoft.ML.FastTree at
+        // https://www.nuget.org/packages/Microsoft.ML.FastTree/
         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(1000);
 
-            // 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 trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
 
             // Define trainer options.
@@ -43,17 +46,21 @@ public static void Example()
             // Train the model.
             var model = pipeline.Fit(trainingData);
 
-            // Create testing data. Use different random seed to make it different from training data.
-            var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
+            // Create testing data. Use different random seed to make it different
+            // from training data.
+            var testData = mlContext.Data.LoadFromEnumerable(
+                GenerateRandomDataPoints(500, seed:123));
 
             // Run the model on test data set.
             var transformedTestData = model.Transform(testData);
 
             // Take the top 5 rows.
-            var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
+            var topTransformedTestData = mlContext.Data.TakeRows(
+                transformedTestData, 5);
 
             // Convert IDataView object to a list.
-            var predictions = mlContext.Data.CreateEnumerable<Prediction>(topTransformedTestData, reuseRowObject: false).ToList();
+            var predictions = mlContext.Data.CreateEnumerable<Prediction>(
+                topTransformedTestData, reuseRowObject: false).ToList();
 
             // Print 5 predictions.
             foreach (var p in predictions)
@@ -75,7 +82,8 @@ public static void Example()
             //   NDCG: @1:0.96, @2:0.95, @3:0.97
         }
 
-        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();
@@ -87,13 +95,16 @@ 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, 50).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, 50).Select(
+                        x => randomFloat() + x * 0.1f).ToArray()
                 };
             }
         }
 
-        // Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
+        // Example with label, groupId, and 50 feature values. A data set is a
+        // collection of such examples.
         private class DataPoint
         {
             [KeyType(5)]
@@ -116,8 +127,12 @@ private class Prediction
         // Pretty-print RankerMetrics objects.
         public static void PrintMetrics(RankingMetrics metrics)
         {
-            Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
-            Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
+            Console.WriteLine("DCG: " + string.Join(", ",
+                metrics.DiscountedCumulativeGains.Select(
+                    (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
+            Console.WriteLine("NDCG: " + string.Join(", ",
+                metrics.NormalizedDiscountedCumulativeGains.Select(
+                    (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
         }
     }
 }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTreeWithOptions.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTreeWithOptions.tt
index b4a65602aa..ce1140edf6 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTreeWithOptions.tt
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/FastTreeWithOptions.tt
@@ -16,8 +16,9 @@ string TrainerOptions = @"FastTreeRankingTrainer.Options
 
 string OptionsInclude = "using Microsoft.ML.Trainers.FastTree;";
 string Comments= @"
-        // This example requires installation of additional NuGet package
-        // <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>.";
+        // This example requires installation of additional NuGet package for 
+        // Microsoft.ML.FastTree at
+        // https://www.nuget.org/packages/Microsoft.ML.FastTree/";
 
 string ExpectedOutputPerInstance = @"// Expected output:
             //   Label: 5, Score: 8.807633
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.cs
index 3b6e542ef7..d6b3945d2b 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.cs
@@ -8,17 +8,22 @@ namespace Samples.Dynamic.Trainers.Ranking
 {
     public static class LightGbm
     {
+        // This example requires installation of additional NuGet package for 
+        // Microsoft.ML.FastTree at
+        // https://www.nuget.org/packages/Microsoft.ML.FastTree/
         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(1000);
 
-            // 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 trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
 
             // Define the trainer.
@@ -27,39 +32,44 @@ public static void Example()
             // Train the model.
             var model = pipeline.Fit(trainingData);
 
-            // Create testing data. Use different random seed to make it different from training data.
-            var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
+            // Create testing data. Use different random seed to make it different
+			// from training data.
+            var testData = mlContext.Data.LoadFromEnumerable(
+                GenerateRandomDataPoints(500, seed:123));
 
             // Run the model on test data set.
             var transformedTestData = model.Transform(testData);
 
             // Take the top 5 rows.
-            var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
+            var topTransformedTestData = mlContext.Data.TakeRows(
+                transformedTestData, 5);
 
             // Convert IDataView object to a list.
-            var predictions = mlContext.Data.CreateEnumerable<Prediction>(topTransformedTestData, reuseRowObject: false).ToList();
+            var predictions = mlContext.Data.CreateEnumerable<Prediction>(
+                topTransformedTestData, reuseRowObject: false).ToList();
 
             // Print 5 predictions.
             foreach (var p in predictions)
                 Console.WriteLine($"Label: {p.Label}, Score: {p.Score}");
 
             // Expected output:
-            //   Label: 5, Score: 2.195333
-            //   Label: 4, Score: 0.2596574
-            //   Label: 4, Score: -2.168355
-            //   Label: 1, Score: -3.074823
-            //   Label: 1, Score: -1.523607
+            //   Label: 5, Score: 2.493263
+            //   Label: 1, Score: -4.528436
+            //   Label: 3, Score: -3.002865
+            //   Label: 3, Score: -2.151812
+            //   Label: 1, Score: -4.089102
 
             // Evaluate the overall metrics.
             var metrics = mlContext.Ranking.Evaluate(transformedTestData);
             PrintMetrics(metrics);
             
             // Expected output:
-            //   DCG: @1:26.03, @2:37.57, @3:45.83
-            //   NDCG: @1:0.61, @2:0.57, @3:0.59
+            //   DCG: @1:41.95, @2:63.76, @3:75.97
+            //   NDCG: @1:0.99, @2:0.99, @3:0.99
         }
 
-        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();
@@ -71,13 +81,16 @@ 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, 50).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, 50).Select(
+					    x => randomFloat() + x * 0.1f).ToArray()
                 };
             }
         }
 
-        // Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
+        // Example with label, groupId, and 50 feature values. A data set is a
+        // collection of such examples.
         private class DataPoint
         {
             [KeyType(5)]
@@ -100,8 +113,12 @@ private class Prediction
         // Pretty-print RankerMetrics objects.
         public static void PrintMetrics(RankingMetrics metrics)
         {
-            Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
-            Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
+            Console.WriteLine("DCG: " + string.Join(", ",
+                metrics.DiscountedCumulativeGains.Select(
+                    (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
+            Console.WriteLine("NDCG: " + string.Join(", ",
+                metrics.NormalizedDiscountedCumulativeGains.Select(
+                    (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
         }
     }
 }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.tt
index 136cad7843..f8c628c26d 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.tt
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.tt
@@ -6,8 +6,9 @@ string TrainerOptions = null;
 
 string OptionsInclude = "";
 string Comments= @"
-        // This example requires installation of additional NuGet package
-        // <a href=""https://www.nuget.org/packages/Microsoft.ML.LightGbm/"">Microsoft.ML.LightGbm</a>.";
+        // This example requires installation of additional NuGet package for 
+        // Microsoft.ML.FastTree at
+        // https://www.nuget.org/packages/Microsoft.ML.FastTree/";
 
 string ExpectedOutputPerInstance = @"// Expected output:
             //   Label: 5, Score: 2.493263
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbmWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbmWithOptions.cs
index 1c21c7704a..4859cd7b81 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbmWithOptions.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbmWithOptions.cs
@@ -9,19 +9,22 @@ namespace Samples.Dynamic.Trainers.Ranking
 {
     public static class LightGbmWithOptions
     {
-        // This example requires installation of additional NuGet package
-        // <a href="https://www.nuget.org/packages/Microsoft.ML.LightGbm/">Microsoft.ML.LightGbm</a>.
+        // This example requires installation of additional NuGet package for 
+        // Microsoft.ML.FastTree at
+        // https://www.nuget.org/packages/Microsoft.ML.FastTree/
         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(1000);
 
-            // 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 trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
 
             // Define trainer options.
@@ -44,17 +47,21 @@ public static void Example()
             // Train the model.
             var model = pipeline.Fit(trainingData);
 
-            // Create testing data. Use different random seed to make it different from training data.
-            var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
+            // Create testing data. Use different random seed to make it different
+            // from training data.
+            var testData = mlContext.Data.LoadFromEnumerable(
+                GenerateRandomDataPoints(500, seed:123));
 
             // Run the model on test data set.
             var transformedTestData = model.Transform(testData);
 
             // Take the top 5 rows.
-            var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
+            var topTransformedTestData = mlContext.Data.TakeRows(
+                transformedTestData, 5);
 
             // Convert IDataView object to a list.
-            var predictions = mlContext.Data.CreateEnumerable<Prediction>(topTransformedTestData, reuseRowObject: false).ToList();
+            var predictions = mlContext.Data.CreateEnumerable<Prediction>(
+                topTransformedTestData, reuseRowObject: false).ToList();
 
             // Print 5 predictions.
             foreach (var p in predictions)
@@ -76,7 +83,8 @@ public static void Example()
             //   NDCG: @1:0.69, @2:0.72, @3:0.74
         }
 
-        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();
@@ -88,13 +96,16 @@ 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, 50).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, 50).Select(
+					    x => randomFloat() + x * 0.1f).ToArray()
                 };
             }
         }
 
-        // Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
+        // Example with label, groupId, and 50 feature values. A data set is a
+        // collection of such examples.
         private class DataPoint
         {
             [KeyType(5)]
@@ -117,8 +128,12 @@ private class Prediction
         // Pretty-print RankerMetrics objects.
         public static void PrintMetrics(RankingMetrics metrics)
         {
-            Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
-            Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
+            Console.WriteLine("DCG: " + string.Join(", ",
+                metrics.DiscountedCumulativeGains.Select(
+                    (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
+            Console.WriteLine("NDCG: " + string.Join(", ",
+                metrics.NormalizedDiscountedCumulativeGains.Select(
+                    (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
         }
     }
 }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbmWithOptions.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbmWithOptions.tt
index 0de6d61d6d..939260bd94 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbmWithOptions.tt
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbmWithOptions.tt
@@ -17,8 +17,9 @@ string TrainerOptions = @"LightGbmRankingTrainer.Options
 
 string OptionsInclude = "using Microsoft.ML.Trainers.LightGbm;";
 string Comments= @"
-        // This example requires installation of additional NuGet package
-        // <a href=""https://www.nuget.org/packages/Microsoft.ML.LightGbm/"">Microsoft.ML.LightGbm</a>.";
+        // This example requires installation of additional NuGet package for 
+        // Microsoft.ML.FastTree at
+        // https://www.nuget.org/packages/Microsoft.ML.FastTree/";
 
 string ExpectedOutputPerInstance = @"// Expected output:
             //   Label: 5, Score: 0.05836755
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/PermutationFeatureImportance.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/PermutationFeatureImportance.cs
index 41928a70ee..04b82151ac 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/PermutationFeatureImportance.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/PermutationFeatureImportance.cs
@@ -9,8 +9,9 @@ public static class PermutationFeatureImportance
     {
         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(seed:1);
 
             // Create sample data.
@@ -19,12 +20,15 @@ public static void Example()
             // Load the sample data as an IDataView.
             var data = mlContext.Data.LoadFromEnumerable(samples);
 
-            // Define a training pipeline that concatenates features into a vector, normalizes them, and then
-            // trains a linear model.
-            var featureColumns = new string[] { nameof(Data.Feature1), nameof(Data.Feature2) };
-            var pipeline = mlContext.Transforms.Concatenate("Features", featureColumns)
+            // Define a training pipeline that concatenates features into a vector,
+            // normalizes them, and then trains a linear model.
+            var featureColumns = new string[] { nameof(Data.Feature1), nameof(
+                Data.Feature2) };
+            var pipeline = mlContext.Transforms.Concatenate("Features",
+                featureColumns)
                     .Append(mlContext.Transforms.Conversion.MapValueToKey("Label"))
-                    .Append(mlContext.Transforms.Conversion.MapValueToKey("GroupId"))
+                    .Append(mlContext.Transforms.Conversion.MapValueToKey(
+                        "GroupId"))
                     .Append(mlContext.Transforms.NormalizeMinMax("Features"))
                     .Append(mlContext.Ranking.Trainers.FastTree());
 
@@ -37,18 +41,24 @@ public static void Example()
             // Extract the predictor.
             var linearPredictor = model.LastTransformer;
 
-            // Compute the permutation metrics for the linear model using the normalized data.
+            // Compute the permutation metrics for the linear model using the
+            // normalized data.
             var permutationMetrics = mlContext.Ranking.PermutationFeatureImportance(
                 linearPredictor, transformedData, permutationCount: 30);
 
-            // Now let's look at which features are most important to the model overall.
-            // Get the feature indices sorted by their impact on NDCG@1.
-            var sortedIndices = permutationMetrics.Select((metrics, index) => new { index, metrics.NormalizedDiscountedCumulativeGains})
-                .OrderByDescending(feature => Math.Abs(feature.NormalizedDiscountedCumulativeGains[0].Mean))
+            // Now let's look at which features are most important to the model
+            // overall. Get the feature indices sorted by their impact on NDCG@1.
+            var sortedIndices = permutationMetrics.Select((metrics, index) => new {
+                index, metrics.NormalizedDiscountedCumulativeGains})
+                .OrderByDescending(feature => Math.Abs(
+                    feature.NormalizedDiscountedCumulativeGains[0].Mean))
+
                 .Select(feature => feature.index);
 
-            Console.WriteLine("Feature\tChange in NDCG@1\t95% Confidence in the Mean Change in NDCG@1");
-            var ndcg = permutationMetrics.Select(x => x.NormalizedDiscountedCumulativeGains).ToArray();
+            Console.WriteLine("Feature\tChange in NDCG@1\t95% Confidence in the" +
+                "Mean Change in NDCG@1");
+            var ndcg = permutationMetrics.Select(
+                x => x.NormalizedDiscountedCumulativeGains).ToArray();
             foreach (int i in sortedIndices)
             {
                 Console.WriteLine("{0}\t{1:G4}\t{2:G4}",
@@ -78,14 +88,25 @@ private class Data
         /// Generate an enumerable of Data objects, creating the label as a simple
         /// linear combination of the features.
         /// </summary>
+        /// 
         /// <param name="nExamples">The number of examples.</param>
-        /// <param name="bias">The bias, or offset, in the calculation of the label.</param>
-        /// <param name="weight1">The weight to multiply the first feature with to compute the label.</param>
-        /// <param name="weight2">The weight to multiply the second feature with to compute the label.</param>
-        /// <param name="seed">The seed for generating feature values and label noise.</param>
+        /// 
+        /// <param name="bias">The bias, or offset, in the calculation of the label.
+        /// </param>
+        /// 
+        /// <param name="weight1">The weight to multiply the first feature with to
+        /// compute the label.</param>
+        /// 
+        /// <param name="weight2">The weight to multiply the second feature with to
+        /// compute the label.</param>
+        /// 
+        /// <param name="seed">The seed for generating feature values and label
+        /// noise.</param>
+        /// 
         /// <returns>An enumerable of Data objects.</returns>
         private static IEnumerable<Data> GenerateData(int nExamples = 10000,
-            double bias = 0, double weight1 = 1, double weight2 = 2, int seed = 1, int groupSize = 5)
+            double bias = 0, double weight1 = 1, double weight2 = 2, int seed = 1,
+                int groupSize = 5)
         {
             var rng = new Random(seed);
             var max = bias + 4.5 * weight1 + 4.5 * weight2 + 0.5;
@@ -99,7 +120,8 @@ private static IEnumerable<Data> GenerateData(int nExamples = 10000,
                 };
 
                 // Create a noisy label.
-                var value = (float)(bias + weight1 * data.Feature1 + weight2 * data.Feature2 + rng.NextDouble() - 0.5);
+                var value = (float)(bias + weight1 * data.Feature1 + weight2 * 
+                    data.Feature2 + rng.NextDouble() - 0.5);
                 if (value < max / 3)
                     data.Label = 0;
                 else if (value < 2 * max / 3)
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/Ranking.ttinclude b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/Ranking.ttinclude
index cc90a58187..05ed4a68b4 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/Ranking.ttinclude
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/Ranking.ttinclude
@@ -13,15 +13,17 @@ namespace Samples.Dynamic.Trainers.Ranking
     {<#=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(1000);
 
-            // 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 trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
 
 <# if (TrainerOptions == null) { #>
@@ -38,17 +40,21 @@ namespace Samples.Dynamic.Trainers.Ranking
             // Train the model.
             var model = pipeline.Fit(trainingData);
 
-            // Create testing data. Use different random seed to make it different from training data.
-            var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
+            // Create testing data. Use different random seed to make it different
+            // from training data.
+            var testData = mlContext.Data.LoadFromEnumerable(
+                GenerateRandomDataPoints(500, seed:123));
 
             // Run the model on test data set.
             var transformedTestData = model.Transform(testData);
 
             // Take the top 5 rows.
-            var topTransformedTestData = mlContext.Data.TakeRows(transformedTestData, 5);
+            var topTransformedTestData = mlContext.Data.TakeRows(
+                transformedTestData, 5);
 
             // Convert IDataView object to a list.
-            var predictions = mlContext.Data.CreateEnumerable<Prediction>(topTransformedTestData, reuseRowObject: false).ToList();
+            var predictions = mlContext.Data.CreateEnumerable<Prediction>(
+                topTransformedTestData, reuseRowObject: false).ToList();
 
             // Print 5 predictions.
             foreach (var p in predictions)
@@ -63,7 +69,8 @@ namespace Samples.Dynamic.Trainers.Ranking
             <#=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();
@@ -75,13 +82,16 @@ namespace Samples.Dynamic.Trainers.Ranking
                     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, 50).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, 50).Select(
+					    x => randomFloat() + x * 0.1f).ToArray()
                 };
             }
         }
 
-        // Example with label, groupId, and 50 feature values. A data set is a collection of such examples.
+        // Example with label, groupId, and 50 feature values. A data set is a
+        // collection of such examples.
         private class DataPoint
         {
             [KeyType(5)]
@@ -104,8 +114,12 @@ namespace Samples.Dynamic.Trainers.Ranking
         // Pretty-print RankerMetrics objects.
         public static void PrintMetrics(RankingMetrics metrics)
         {
-            Console.WriteLine($"DCG: {string.Join(", ", metrics.DiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
-            Console.WriteLine($"NDCG: {string.Join(", ", metrics.NormalizedDiscountedCumulativeGains.Select((d, i) => $"@{i + 1}:{d:F2}").ToArray())}");
+            Console.WriteLine("DCG: " + string.Join(", ",
+                metrics.DiscountedCumulativeGains.Select(
+                    (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
+            Console.WriteLine("NDCG: " + string.Join(", ",
+                metrics.NormalizedDiscountedCumulativeGains.Select(
+                    (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
         }
     }
-}
\ No newline at end of file
+}