diff --git a/src/Microsoft.ML.FastTree/FastTree.cs b/src/Microsoft.ML.FastTree/FastTree.cs
index d45f3ab469..e61a61e59c 100644
--- a/src/Microsoft.ML.FastTree/FastTree.cs
+++ b/src/Microsoft.ML.FastTree/FastTree.cs
@@ -3112,7 +3112,7 @@ private protected virtual bool SaveAsOnnx(OnnxContext ctx, string[] outputNames,
             }
 
             string opType = "TreeEnsembleRegressor";
-            string scoreVarName = (Utils.Size(outputNames) == 2) ? outputNames[1] : outputNames[0]; // Get Score from PredictedLabel and/or Score columns
+            string scoreVarName = (Utils.Size(outputNames) >= 2) ? outputNames[1] : outputNames[0]; // Get Score from PredictedLabel and/or Score columns
             var node = ctx.CreateNode(opType, new[] { featureColumn }, new[] { scoreVarName }, ctx.GetNodeName(opType));
 
             node.AddAttribute("post_transform", PostTransform.None.GetDescription());
diff --git a/src/Microsoft.ML.StandardTrainers/Standard/LinearModelParameters.cs b/src/Microsoft.ML.StandardTrainers/Standard/LinearModelParameters.cs
index d2dea6f60e..8f1745b2a0 100644
--- a/src/Microsoft.ML.StandardTrainers/Standard/LinearModelParameters.cs
+++ b/src/Microsoft.ML.StandardTrainers/Standard/LinearModelParameters.cs
@@ -138,7 +138,7 @@ private protected virtual bool SaveAsOnnx(OnnxContext ctx, string[] outputs, str
             Host.CheckValue(ctx, nameof(ctx));
             Host.Check(Utils.Size(outputs) >= 1);
             string opType = "LinearRegressor";
-            string scoreVarName = (Utils.Size(outputs) == 2) ? outputs[1] : outputs[0]; // Get Score from PredictedLabel and/or Score columns
+            string scoreVarName = (Utils.Size(outputs) >= 2) ? outputs[1] : outputs[0]; // Get Score from PredictedLabel and/or Score columns
             var node = ctx.CreateNode(opType, new[] { featureColumn }, new[] { scoreVarName }, ctx.GetNodeName(opType));
             // Selection of logit or probit output transform. enum {'NONE', 'LOGIT', 'PROBIT}
             node.AddAttribute("post_transform", "NONE");
diff --git a/src/Microsoft.ML.StandardTrainers/Standard/MulticlassClassification/OneVersusAllTrainer.cs b/src/Microsoft.ML.StandardTrainers/Standard/MulticlassClassification/OneVersusAllTrainer.cs
index 185da7a4eb..54f74abad0 100644
--- a/src/Microsoft.ML.StandardTrainers/Standard/MulticlassClassification/OneVersusAllTrainer.cs
+++ b/src/Microsoft.ML.StandardTrainers/Standard/MulticlassClassification/OneVersusAllTrainer.cs
@@ -563,7 +563,7 @@ public string[] SaveAsOnnxPreProcess(OnnxContext ctx, string featureColumn, bool
                         var clipNode = ctx.CreateNode(opType, new[] { clipInput, zeroVar }, new[] { outputs[i] }, ctx.GetNodeName(opType), "");
                     }
                     else
-                        outputs[i] = predictorOutputNames[2];
+                        outputs[i] = predictorOutputNames[1];
                 }
                 return outputs;
             }
@@ -659,7 +659,7 @@ public override JToken SaveAsPfa(BoundPfaContext ctx, JToken input)
 
             public override bool SaveAsOnnx(OnnxContext ctx, string[] outputNames, string featureColumn)
             {
-                var probabilityOutputs = base.SaveAsOnnxPreProcess(ctx, featureColumn, true);
+                var probabilityOutputs = base.SaveAsOnnxPreProcess(ctx, featureColumn, false);
 
                 string opType = "Concat";
                 var concatOutput = ctx.AddIntermediateVariable(NumberDataViewType.Single, "ConcatOutput", true);
@@ -794,22 +794,27 @@ public override bool SaveAsOnnx(OnnxContext ctx, string[] outputNames, string fe
 
                 opType = "Sum";
                 var sumOutput = ctx.AddIntermediateVariable(NumberDataViewType.Single, "SumOfScores", true);
-                var sumNode = ctx.CreateNode(opType, probabilityOutputs, new[] { sumOutput }, ctx.GetNodeName(opType), "");
+                ctx.CreateNode(opType, probabilityOutputs, new[] { sumOutput }, ctx.GetNodeName(opType), "");
 
                 opType = "Cast";
-                var castOutput = ctx.AddIntermediateVariable(BooleanDataViewType.Instance, "IsSumZero", true);
+                var castOutput = ctx.AddIntermediateVariable(BooleanDataViewType.Instance, "CastOutput", true);
                 var castNode = ctx.CreateNode(opType, sumOutput, castOutput, ctx.GetNodeName(opType), "");
                 var t = InternalDataKindExtensions.ToInternalDataKind(DataKind.Boolean).ToType();
                 castNode.AddAttribute("to", t);
 
+                opType = "Not";
+                var notOutput = ctx.AddIntermediateVariable(null, "IsSumZero", true);
+                ctx.CreateNode(opType, castOutput, notOutput, ctx.GetNodeName(opType), "");
+
+                opType = "Cast";
                 var castIsZeroSumToFloat = ctx.AddIntermediateVariable(BooleanDataViewType.Instance, "IsSumZeroAsFloat", true);
-                var castIsZeroSumToFloatNode = ctx.CreateNode(opType, castOutput, castIsZeroSumToFloat, ctx.GetNodeName(opType), "");
+                var castIsZeroSumToFloatNode = ctx.CreateNode(opType, notOutput, castIsZeroSumToFloat, ctx.GetNodeName(opType), "");
                 var t1 = InternalDataKindExtensions.ToInternalDataKind(DataKind.Single).ToType();
                 castIsZeroSumToFloatNode.AddAttribute("to", t1);
 
                 opType = "Sum";
                 var sumOutputNonZero = ctx.AddIntermediateVariable(NumberDataViewType.Single, "SumOfScoresNonZero", true);
-                var sumOutputNonZeroNode = ctx.CreateNode(opType, new[] { sumOutput, castIsZeroSumToFloat },
+                ctx.CreateNode(opType, new[] { sumOutput, castIsZeroSumToFloat },
                     new[] { sumOutputNonZero }, ctx.GetNodeName(opType), "");
 
                 string[] divOutputs = new string[Predictors.Length];
diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs
index e22b82133f..b52196a56e 100644
--- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs
+++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs
@@ -1319,8 +1319,22 @@ public void MulticlassTrainersOnnxConversionTest()
             {
                 mlContext.MulticlassClassification.Trainers.LbfgsMaximumEntropy(),
                 mlContext.MulticlassClassification.Trainers.NaiveBayes(),
+                mlContext.MulticlassClassification.Trainers.OneVersusAll(
+                    mlContext.BinaryClassification.Trainers.AveragedPerceptron()),
+                mlContext.MulticlassClassification.Trainers.OneVersusAll(
+                    mlContext.BinaryClassification.Trainers.AveragedPerceptron(), useProbabilities:false),
+                mlContext.MulticlassClassification.Trainers.OneVersusAll(
+                    mlContext.BinaryClassification.Trainers.LbfgsLogisticRegression()),
                 mlContext.MulticlassClassification.Trainers.OneVersusAll(
                     mlContext.BinaryClassification.Trainers.LbfgsLogisticRegression(), useProbabilities:false),
+                mlContext.MulticlassClassification.Trainers.OneVersusAll(
+                    mlContext.BinaryClassification.Trainers.LinearSvm()),
+                mlContext.MulticlassClassification.Trainers.OneVersusAll(
+                    mlContext.BinaryClassification.Trainers.LinearSvm(), useProbabilities:false),
+                mlContext.MulticlassClassification.Trainers.OneVersusAll(
+                    mlContext.BinaryClassification.Trainers.FastForest()),
+                mlContext.MulticlassClassification.Trainers.OneVersusAll(
+                    mlContext.BinaryClassification.Trainers.FastForest(), useProbabilities:false),
                 mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy(),
                 mlContext.MulticlassClassification.Trainers.SdcaNonCalibrated()
             };
@@ -1357,7 +1371,8 @@ public void MulticlassTrainersOnnxConversionTest()
                     var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
-                    CompareSelectedScalarColumns<uint>(transformedData.Schema[5].Name, outputNames[2], transformedData, onnxResult);
+                    CompareSelectedScalarColumns<uint>(transformedData.Schema[5].Name, outputNames[2], transformedData, onnxResult); //compare predicted labels
+                    CompareSelectedR4VectorColumns(transformedData.Schema[6].Name, outputNames[3], transformedData, onnxResult, 4); //compare scores
                 }
             }
             Done();