diff --git a/src/Microsoft.ML.OnnxConverter/SaveOnnxCommand.cs b/src/Microsoft.ML.OnnxConverter/SaveOnnxCommand.cs
index 966fe255f4..a2aa961b22 100644
--- a/src/Microsoft.ML.OnnxConverter/SaveOnnxCommand.cs
+++ b/src/Microsoft.ML.OnnxConverter/SaveOnnxCommand.cs
@@ -201,7 +201,7 @@ internal static ModelProto ConvertTransformListToOnnxModel(OnnxContextImpl ctx,
                 ch.Check(variableName != null, "The targeted pipeline can not be fully converted into a well-defined ONNX model. " +
                     "Please check if all steps in that pipeline are convertible to ONNX " +
                     "and all necessary variables are not dropped (via command line arguments).");
-                var trueVariableName = ctx.AddIntermediateVariable(null, idataviewColumnName + ".onnx", true);
+                var trueVariableName = ctx.AddIntermediateVariable(null, idataviewColumnName + ".output", true);
                 ctx.CreateNode("Identity", variableName, trueVariableName, ctx.GetNodeName("Identity"), "");
                 ctx.AddOutputVariable(outputData.Schema[i].Type, trueVariableName);
             }
diff --git a/src/Microsoft.ML.OnnxTransformer/OnnxTransform.cs b/src/Microsoft.ML.OnnxTransformer/OnnxTransform.cs
index 60d82c6a76..fd213845b4 100644
--- a/src/Microsoft.ML.OnnxTransformer/OnnxTransform.cs
+++ b/src/Microsoft.ML.OnnxTransformer/OnnxTransform.cs
@@ -410,9 +410,14 @@ public Mapper(OnnxTransformer parent, DataViewSchema inputSchema) :
 
             protected override DataViewSchema.DetachedColumn[] GetOutputColumnsCore()
             {
+                var stdSuffix = ".output";
                 var info = new DataViewSchema.DetachedColumn[_parent.Outputs.Length];
                 for (int i = 0; i < _parent.Outputs.Length; i++)
-                    info[i] = new DataViewSchema.DetachedColumn(_parent.Outputs[i], _parent.OutputTypes[i], null);
+                {
+                    var onnxOutputName = _parent.Outputs[i];
+                    var columnName = onnxOutputName.EndsWith(stdSuffix) ? onnxOutputName.Replace(stdSuffix, "") : onnxOutputName;
+                    info[i] = new DataViewSchema.DetachedColumn(columnName, _parent.OutputTypes[i], null);
+                }
                 return info;
             }
 
diff --git a/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ExcludeVariablesInOnnxConversion.txt b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ExcludeVariablesInOnnxConversion.txt
index d37a3a39db..7b0036d538 100644
--- a/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ExcludeVariablesInOnnxConversion.txt
+++ b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ExcludeVariablesInOnnxConversion.txt
@@ -450,7 +450,7 @@
           "PredictedLabel"
         ],
         "output": [
-          "PredictedLabel.onnx"
+          "PredictedLabel.output"
         ],
         "name": "Identity",
         "opType": "Identity"
@@ -460,7 +460,7 @@
           "Score"
         ],
         "output": [
-          "Score.onnx"
+          "Score.output"
         ],
         "name": "Identity0",
         "opType": "Identity"
@@ -470,7 +470,7 @@
           "Probability"
         ],
         "output": [
-          "Probability.onnx"
+          "Probability.output"
         ],
         "name": "Identity1",
         "opType": "Identity"
@@ -533,7 +533,7 @@
     ],
     "output": [
       {
-        "name": "PredictedLabel.onnx",
+        "name": "PredictedLabel.output",
         "type": {
           "tensorType": {
             "elemType": 9,
@@ -551,7 +551,7 @@
         }
       },
       {
-        "name": "Score.onnx",
+        "name": "Score.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -569,7 +569,7 @@
         }
       },
       {
-        "name": "Probability.onnx",
+        "name": "Probability.output",
         "type": {
           "tensorType": {
             "elemType": 1,
diff --git a/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/LightGbmBinaryClassificationOnnxConversionTest.txt b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/LightGbmBinaryClassificationOnnxConversionTest.txt
index 9d3ac3fd55..8bac688f71 100644
--- a/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/LightGbmBinaryClassificationOnnxConversionTest.txt
+++ b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/LightGbmBinaryClassificationOnnxConversionTest.txt
@@ -360,7 +360,7 @@
           "FeatureVector0"
         ],
         "output": [
-          "FeatureVector.onnx"
+          "FeatureVector.output"
         ],
         "name": "Identity",
         "opType": "Identity"
@@ -370,7 +370,7 @@
           "Target"
         ],
         "output": [
-          "Target.onnx"
+          "Target.output"
         ],
         "name": "Identity0",
         "opType": "Identity"
@@ -380,7 +380,7 @@
           "Score"
         ],
         "output": [
-          "Score.onnx"
+          "Score.output"
         ],
         "name": "Identity1",
         "opType": "Identity"
@@ -427,7 +427,7 @@
     ],
     "output": [
       {
-        "name": "FeatureVector.onnx",
+        "name": "FeatureVector.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -445,7 +445,7 @@
         }
       },
       {
-        "name": "Target.onnx",
+        "name": "Target.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -463,7 +463,7 @@
         }
       },
       {
-        "name": "Score.onnx",
+        "name": "Score.output",
         "type": {
           "tensorType": {
             "elemType": 1,
diff --git a/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/LogisticRegressionSaveModelToOnnxTest.txt b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/LogisticRegressionSaveModelToOnnxTest.txt
index 253b2ed684..f41b69f8c4 100644
--- a/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/LogisticRegressionSaveModelToOnnxTest.txt
+++ b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/LogisticRegressionSaveModelToOnnxTest.txt
@@ -104,7 +104,7 @@
           "FeatureVector0"
         ],
         "output": [
-          "FeatureVector.onnx"
+          "FeatureVector.output"
         ],
         "name": "Identity",
         "opType": "Identity"
@@ -114,7 +114,7 @@
           "Target"
         ],
         "output": [
-          "Target.onnx"
+          "Target.output"
         ],
         "name": "Identity0",
         "opType": "Identity"
@@ -124,7 +124,7 @@
           "Score"
         ],
         "output": [
-          "Score.onnx"
+          "Score.output"
         ],
         "name": "Identity1",
         "opType": "Identity"
@@ -171,7 +171,7 @@
     ],
     "output": [
       {
-        "name": "FeatureVector.onnx",
+        "name": "FeatureVector.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -189,7 +189,7 @@
         }
       },
       {
-        "name": "Target.onnx",
+        "name": "Target.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -207,7 +207,7 @@
         }
       },
       {
-        "name": "Score.onnx",
+        "name": "Score.output",
         "type": {
           "tensorType": {
             "elemType": 1,
diff --git a/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ModelWithLessIO.txt b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ModelWithLessIO.txt
index 22273e89f4..e265a3b7c9 100644
--- a/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ModelWithLessIO.txt
+++ b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ModelWithLessIO.txt
@@ -677,27 +677,27 @@
             "floats": [
               -0.9850374,
               -1,
-              -0.428571433,
+              -0.42857143,
               0.05882353,
               0.9655172,
-              0.478260875,
-              7.006492E-45,
+              0.47826087,
+              7E-45,
               0.9354839,
               -0.837172,
-              -0.896625638,
+              -0.89662564,
               -0.3455931,
-              0.223126009,
+              0.22312601,
               0.8040303,
               0.60825175,
               -0.06932944,
-              -0.402043074,
+              -0.40204307,
               -0.7417274,
-              -0.408434927,
+              -0.40843493,
               0.7105746,
               0.1875386,
               0.7631735,
-              0.706173241,
-              0.625906467,
+              0.70617324,
+              0.62590647,
               -0.35968104
             ],
             "type": "FLOATS"
@@ -776,7 +776,7 @@
           "PredictedLabel"
         ],
         "output": [
-          "PredictedLabel.onnx"
+          "PredictedLabel.output"
         ],
         "name": "Identity",
         "opType": "Identity"
@@ -786,7 +786,7 @@
           "Score"
         ],
         "output": [
-          "Score.onnx"
+          "Score.output"
         ],
         "name": "Identity0",
         "opType": "Identity"
@@ -796,7 +796,7 @@
           "Probability"
         ],
         "output": [
-          "Probability.onnx"
+          "Probability.output"
         ],
         "name": "Identity1",
         "opType": "Identity"
@@ -859,7 +859,7 @@
     ],
     "output": [
       {
-        "name": "PredictedLabel.onnx",
+        "name": "PredictedLabel.output",
         "type": {
           "tensorType": {
             "elemType": 9,
@@ -877,7 +877,7 @@
         }
       },
       {
-        "name": "Score.onnx",
+        "name": "Score.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -895,7 +895,7 @@
         }
       },
       {
-        "name": "Probability.onnx",
+        "name": "Probability.output",
         "type": {
           "tensorType": {
             "elemType": 1,
diff --git a/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/OneHotBagPipeline.txt b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/OneHotBagPipeline.txt
index 6822e97818..93550ceda7 100644
--- a/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/OneHotBagPipeline.txt
+++ b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/OneHotBagPipeline.txt
@@ -324,8 +324,8 @@
           {
             "name": "target_weights",
             "floats": [
-              0.504761934,
-              -0.979112267
+              0.50476193,
+              -0.97911227
             ],
             "type": "FLOATS"
           }
@@ -403,7 +403,7 @@
           "Label"
         ],
         "output": [
-          "Label.onnx"
+          "Label.output"
         ],
         "name": "Identity",
         "opType": "Identity"
@@ -413,7 +413,7 @@
           "F1"
         ],
         "output": [
-          "F1.onnx"
+          "F1.output"
         ],
         "name": "Identity0",
         "opType": "Identity"
@@ -423,7 +423,7 @@
           "F22"
         ],
         "output": [
-          "F2.onnx"
+          "F2.output"
         ],
         "name": "Identity1",
         "opType": "Identity"
@@ -433,7 +433,7 @@
           "Features"
         ],
         "output": [
-          "Features.onnx"
+          "Features.output"
         ],
         "name": "Identity2",
         "opType": "Identity"
@@ -443,7 +443,7 @@
           "PredictedLabel"
         ],
         "output": [
-          "PredictedLabel.onnx"
+          "PredictedLabel.output"
         ],
         "name": "Identity3",
         "opType": "Identity"
@@ -453,7 +453,7 @@
           "Score"
         ],
         "output": [
-          "Score.onnx"
+          "Score.output"
         ],
         "name": "Identity4",
         "opType": "Identity"
@@ -463,7 +463,7 @@
           "Probability"
         ],
         "output": [
-          "Probability.onnx"
+          "Probability.output"
         ],
         "name": "Identity5",
         "opType": "Identity"
@@ -544,7 +544,7 @@
     ],
     "output": [
       {
-        "name": "Label.onnx",
+        "name": "Label.output",
         "type": {
           "tensorType": {
             "elemType": 9,
@@ -562,7 +562,7 @@
         }
       },
       {
-        "name": "F1.onnx",
+        "name": "F1.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -580,7 +580,7 @@
         }
       },
       {
-        "name": "F2.onnx",
+        "name": "F2.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -598,7 +598,7 @@
         }
       },
       {
-        "name": "Features.onnx",
+        "name": "Features.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -616,7 +616,7 @@
         }
       },
       {
-        "name": "PredictedLabel.onnx",
+        "name": "PredictedLabel.output",
         "type": {
           "tensorType": {
             "elemType": 9,
@@ -634,7 +634,7 @@
         }
       },
       {
-        "name": "Score.onnx",
+        "name": "Score.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -652,7 +652,7 @@
         }
       },
       {
-        "name": "Probability.onnx",
+        "name": "Probability.output",
         "type": {
           "tensorType": {
             "elemType": 1,
diff --git a/test/BaselineOutput/Common/Onnx/Cluster/BreastCancer/Kmeans.txt b/test/BaselineOutput/Common/Onnx/Cluster/BreastCancer/Kmeans.txt
index 2aa042f0a8..68fbc19f19 100644
--- a/test/BaselineOutput/Common/Onnx/Cluster/BreastCancer/Kmeans.txt
+++ b/test/BaselineOutput/Common/Onnx/Cluster/BreastCancer/Kmeans.txt
@@ -148,7 +148,7 @@
           "Features0"
         ],
         "output": [
-          "Features.onnx"
+          "Features.output"
         ],
         "name": "Identity",
         "opType": "Identity"
@@ -158,7 +158,7 @@
           "PredictedLabel"
         ],
         "output": [
-          "PredictedLabel.onnx"
+          "PredictedLabel.output"
         ],
         "name": "Identity0",
         "opType": "Identity"
@@ -168,7 +168,7 @@
           "Score"
         ],
         "output": [
-          "Score.onnx"
+          "Score.output"
         ],
         "name": "Identity1",
         "opType": "Identity"
@@ -268,7 +268,7 @@
     ],
     "output": [
       {
-        "name": "Features.onnx",
+        "name": "Features.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -286,7 +286,7 @@
         }
       },
       {
-        "name": "PredictedLabel.onnx",
+        "name": "PredictedLabel.output",
         "type": {
           "tensorType": {
             "elemType": 12,
@@ -304,7 +304,7 @@
         }
       },
       {
-        "name": "Score.onnx",
+        "name": "Score.output",
         "type": {
           "tensorType": {
             "elemType": 1,
diff --git a/test/BaselineOutput/Common/Onnx/MultiClassClassification/BreastCancer/MultiClassificationLogisticRegressionSaveModelToOnnxTest.txt b/test/BaselineOutput/Common/Onnx/MultiClassClassification/BreastCancer/MultiClassificationLogisticRegressionSaveModelToOnnxTest.txt
index 9840c142ec..ab04e5921f 100644
--- a/test/BaselineOutput/Common/Onnx/MultiClassClassification/BreastCancer/MultiClassificationLogisticRegressionSaveModelToOnnxTest.txt
+++ b/test/BaselineOutput/Common/Onnx/MultiClassClassification/BreastCancer/MultiClassificationLogisticRegressionSaveModelToOnnxTest.txt
@@ -288,7 +288,7 @@
           "Label0"
         ],
         "output": [
-          "Label.onnx"
+          "Label.output"
         ],
         "name": "Identity",
         "opType": "Identity"
@@ -298,7 +298,7 @@
           "Features0"
         ],
         "output": [
-          "Features.onnx"
+          "Features.output"
         ],
         "name": "Identity0",
         "opType": "Identity"
@@ -308,7 +308,7 @@
           "PredictedLabel"
         ],
         "output": [
-          "PredictedLabel.onnx"
+          "PredictedLabel.output"
         ],
         "name": "Identity1",
         "opType": "Identity"
@@ -318,7 +318,7 @@
           "Score"
         ],
         "output": [
-          "Score.onnx"
+          "Score.output"
         ],
         "name": "Identity2",
         "opType": "Identity"
@@ -378,7 +378,7 @@
     ],
     "output": [
       {
-        "name": "Label.onnx",
+        "name": "Label.output",
         "type": {
           "tensorType": {
             "elemType": 12,
@@ -396,7 +396,7 @@
         }
       },
       {
-        "name": "Features.onnx",
+        "name": "Features.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -414,7 +414,7 @@
         }
       },
       {
-        "name": "PredictedLabel.onnx",
+        "name": "PredictedLabel.output",
         "type": {
           "tensorType": {
             "elemType": 12,
@@ -432,7 +432,7 @@
         }
       },
       {
-        "name": "Score.onnx",
+        "name": "Score.output",
         "type": {
           "tensorType": {
             "elemType": 1,
diff --git a/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.FastTree.FastForestRegressionTrainer.txt b/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.FastTree.FastForestRegressionTrainer.txt
index 35318c2259..3605f27cb9 100644
--- a/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.FastTree.FastForestRegressionTrainer.txt
+++ b/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.FastTree.FastForestRegressionTrainer.txt
@@ -39328,7 +39328,7 @@
           "FeatureVector"
         ],
         "output": [
-          "FeatureVector.onnx"
+          "FeatureVector.output"
         ],
         "name": "Identity",
         "opType": "Identity"
@@ -39338,7 +39338,7 @@
           "Target"
         ],
         "output": [
-          "Target.onnx"
+          "Target.output"
         ],
         "name": "Identity0",
         "opType": "Identity"
@@ -39348,7 +39348,7 @@
           "Score"
         ],
         "output": [
-          "Score.onnx"
+          "Score.output"
         ],
         "name": "Identity1",
         "opType": "Identity"
@@ -39404,7 +39404,7 @@
     ],
     "output": [
       {
-        "name": "FeatureVector.onnx",
+        "name": "FeatureVector.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -39422,7 +39422,7 @@
         }
       },
       {
-        "name": "Target.onnx",
+        "name": "Target.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -39440,7 +39440,7 @@
         }
       },
       {
-        "name": "Score.onnx",
+        "name": "Score.output",
         "type": {
           "tensorType": {
             "elemType": 1,
diff --git a/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.FastTree.FastTreeRegressionTrainer.txt b/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.FastTree.FastTreeRegressionTrainer.txt
index 0f2430bcb4..f82fc040dd 100644
--- a/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.FastTree.FastTreeRegressionTrainer.txt
+++ b/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.FastTree.FastTreeRegressionTrainer.txt
@@ -39317,7 +39317,7 @@
           "FeatureVector"
         ],
         "output": [
-          "FeatureVector.onnx"
+          "FeatureVector.output"
         ],
         "name": "Identity",
         "opType": "Identity"
@@ -39327,7 +39327,7 @@
           "Target"
         ],
         "output": [
-          "Target.onnx"
+          "Target.output"
         ],
         "name": "Identity0",
         "opType": "Identity"
@@ -39337,7 +39337,7 @@
           "Score"
         ],
         "output": [
-          "Score.onnx"
+          "Score.output"
         ],
         "name": "Identity1",
         "opType": "Identity"
@@ -39384,7 +39384,7 @@
     ],
     "output": [
       {
-        "name": "FeatureVector.onnx",
+        "name": "FeatureVector.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -39402,7 +39402,7 @@
         }
       },
       {
-        "name": "Target.onnx",
+        "name": "Target.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -39420,7 +39420,7 @@
         }
       },
       {
-        "name": "Score.onnx",
+        "name": "Score.output",
         "type": {
           "tensorType": {
             "elemType": 1,
diff --git a/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.FastTree.FastTreeTweedieTrainer.txt b/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.FastTree.FastTreeTweedieTrainer.txt
index f3d53a2e14..1526d48766 100644
--- a/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.FastTree.FastTreeTweedieTrainer.txt
+++ b/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.FastTree.FastTreeTweedieTrainer.txt
@@ -39327,7 +39327,7 @@
           "FeatureVector"
         ],
         "output": [
-          "FeatureVector.onnx"
+          "FeatureVector.output"
         ],
         "name": "Identity",
         "opType": "Identity"
@@ -39337,7 +39337,7 @@
           "Target"
         ],
         "output": [
-          "Target.onnx"
+          "Target.output"
         ],
         "name": "Identity0",
         "opType": "Identity"
@@ -39347,7 +39347,7 @@
           "Score"
         ],
         "output": [
-          "Score.onnx"
+          "Score.output"
         ],
         "name": "Identity1",
         "opType": "Identity"
@@ -39394,7 +39394,7 @@
     ],
     "output": [
       {
-        "name": "FeatureVector.onnx",
+        "name": "FeatureVector.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -39412,7 +39412,7 @@
         }
       },
       {
-        "name": "Target.onnx",
+        "name": "Target.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -39430,7 +39430,7 @@
         }
       },
       {
-        "name": "Score.onnx",
+        "name": "Score.output",
         "type": {
           "tensorType": {
             "elemType": 1,
diff --git a/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.LbfgsPoissonRegressionTrainer.txt b/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.LbfgsPoissonRegressionTrainer.txt
index d6528e1225..feee35fcee 100644
--- a/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.LbfgsPoissonRegressionTrainer.txt
+++ b/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.LbfgsPoissonRegressionTrainer.txt
@@ -29,15 +29,15 @@
             "name": "coefficients",
             "floats": [
               0.2037472,
-              0.005167962,
+              0.00516796159,
               0.1929681,
-              0.01041878,
-              -0.000658446748,
-              0.00338159036,
-              0.0447585769,
-              0.00602663541,
-              0.00161118619,
-              -0.00189642713,
+              0.0104187969,
+              -0.0006584526,
+              0.00338159618,
+              0.0447585955,
+              0.00602662656,
+              0.00161118712,
+              -0.0018964255,
               0.2229219
             ],
             "type": "FLOATS"
@@ -67,7 +67,7 @@
           "FeatureVector"
         ],
         "output": [
-          "FeatureVector.onnx"
+          "FeatureVector.output"
         ],
         "name": "Identity",
         "opType": "Identity"
@@ -77,7 +77,7 @@
           "Target"
         ],
         "output": [
-          "Target.onnx"
+          "Target.output"
         ],
         "name": "Identity0",
         "opType": "Identity"
@@ -87,7 +87,7 @@
           "Score"
         ],
         "output": [
-          "Score.onnx"
+          "Score.output"
         ],
         "name": "Identity1",
         "opType": "Identity"
@@ -134,7 +134,7 @@
     ],
     "output": [
       {
-        "name": "FeatureVector.onnx",
+        "name": "FeatureVector.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -152,7 +152,7 @@
         }
       },
       {
-        "name": "Target.onnx",
+        "name": "Target.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -170,7 +170,7 @@
         }
       },
       {
-        "name": "Score.onnx",
+        "name": "Score.output",
         "type": {
           "tensorType": {
             "elemType": 1,
diff --git a/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.LightGbm.LightGbmRegressionTrainer.txt b/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.LightGbm.LightGbmRegressionTrainer.txt
index 3f198e12ed..5da774f9c7 100644
--- a/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.LightGbm.LightGbmRegressionTrainer.txt
+++ b/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.LightGbm.LightGbmRegressionTrainer.txt
@@ -38277,7 +38277,7 @@
           "FeatureVector"
         ],
         "output": [
-          "FeatureVector.onnx"
+          "FeatureVector.output"
         ],
         "name": "Identity",
         "opType": "Identity"
@@ -38287,7 +38287,7 @@
           "Target"
         ],
         "output": [
-          "Target.onnx"
+          "Target.output"
         ],
         "name": "Identity0",
         "opType": "Identity"
@@ -38297,7 +38297,7 @@
           "Score"
         ],
         "output": [
-          "Score.onnx"
+          "Score.output"
         ],
         "name": "Identity1",
         "opType": "Identity"
@@ -38344,7 +38344,7 @@
     ],
     "output": [
       {
-        "name": "FeatureVector.onnx",
+        "name": "FeatureVector.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -38362,7 +38362,7 @@
         }
       },
       {
-        "name": "Target.onnx",
+        "name": "Target.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -38380,7 +38380,7 @@
         }
       },
       {
-        "name": "Score.onnx",
+        "name": "Score.output",
         "type": {
           "tensorType": {
             "elemType": 1,
diff --git a/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.OlsTrainer.txt b/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.OlsTrainer.txt
index cf2a05ff38..eb0d9bc0ca 100644
--- a/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.OlsTrainer.txt
+++ b/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.OlsTrainer.txt
@@ -57,7 +57,7 @@
           "FeatureVector"
         ],
         "output": [
-          "FeatureVector.onnx"
+          "FeatureVector.output"
         ],
         "name": "Identity",
         "opType": "Identity"
@@ -67,7 +67,7 @@
           "Target"
         ],
         "output": [
-          "Target.onnx"
+          "Target.output"
         ],
         "name": "Identity0",
         "opType": "Identity"
@@ -77,7 +77,7 @@
           "Score"
         ],
         "output": [
-          "Score.onnx"
+          "Score.output"
         ],
         "name": "Identity1",
         "opType": "Identity"
@@ -124,7 +124,7 @@
     ],
     "output": [
       {
-        "name": "FeatureVector.onnx",
+        "name": "FeatureVector.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -142,7 +142,7 @@
         }
       },
       {
-        "name": "Target.onnx",
+        "name": "Target.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -160,7 +160,7 @@
         }
       },
       {
-        "name": "Score.onnx",
+        "name": "Score.output",
         "type": {
           "tensorType": {
             "elemType": 1,
diff --git a/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.OnlineGradientDescentTrainer.txt b/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.OnlineGradientDescentTrainer.txt
index 572cf6fac3..209b5f0924 100644
--- a/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.OnlineGradientDescentTrainer.txt
+++ b/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.OnlineGradientDescentTrainer.txt
@@ -57,7 +57,7 @@
           "FeatureVector"
         ],
         "output": [
-          "FeatureVector.onnx"
+          "FeatureVector.output"
         ],
         "name": "Identity",
         "opType": "Identity"
@@ -67,7 +67,7 @@
           "Target"
         ],
         "output": [
-          "Target.onnx"
+          "Target.output"
         ],
         "name": "Identity0",
         "opType": "Identity"
@@ -77,7 +77,7 @@
           "Score"
         ],
         "output": [
-          "Score.onnx"
+          "Score.output"
         ],
         "name": "Identity1",
         "opType": "Identity"
@@ -124,7 +124,7 @@
     ],
     "output": [
       {
-        "name": "FeatureVector.onnx",
+        "name": "FeatureVector.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -142,7 +142,7 @@
         }
       },
       {
-        "name": "Target.onnx",
+        "name": "Target.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -160,7 +160,7 @@
         }
       },
       {
-        "name": "Score.onnx",
+        "name": "Score.output",
         "type": {
           "tensorType": {
             "elemType": 1,
diff --git a/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.SdcaRegressionTrainer.txt b/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.SdcaRegressionTrainer.txt
index 62afef40c6..8f8bb14e77 100644
--- a/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.SdcaRegressionTrainer.txt
+++ b/test/BaselineOutput/Common/Onnx/Regression/Adult/Microsoft.ML.Trainers.SdcaRegressionTrainer.txt
@@ -28,24 +28,24 @@
           {
             "name": "coefficients",
             "floats": [
-              77.0586548,
-              0.008821458,
-              74.8481,
-              -0.0400403664,
-              -0.0316747874,
-              0.0483982079,
-              19.31359,
-              3.932647,
-              0.0116912266,
-              0.09054199,
-              82.77022
+              77.06203,
+              0.00723217,
+              74.84655,
+              -0.0392755158,
+              -0.03449215,
+              0.0474231839,
+              19.3112488,
+              3.93133354,
+              0.008931219,
+              0.09011991,
+              82.76908
             ],
             "type": "FLOATS"
           },
           {
             "name": "intercepts",
             "floats": [
-              373.88443
+              373.885468
             ],
             "type": "FLOATS"
           }
@@ -57,7 +57,7 @@
           "FeatureVector"
         ],
         "output": [
-          "FeatureVector.onnx"
+          "FeatureVector.output"
         ],
         "name": "Identity",
         "opType": "Identity"
@@ -67,7 +67,7 @@
           "Target"
         ],
         "output": [
-          "Target.onnx"
+          "Target.output"
         ],
         "name": "Identity0",
         "opType": "Identity"
@@ -77,7 +77,7 @@
           "Score"
         ],
         "output": [
-          "Score.onnx"
+          "Score.output"
         ],
         "name": "Identity1",
         "opType": "Identity"
@@ -124,7 +124,7 @@
     ],
     "output": [
       {
-        "name": "FeatureVector.onnx",
+        "name": "FeatureVector.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -142,7 +142,7 @@
         }
       },
       {
-        "name": "Target.onnx",
+        "name": "Target.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -160,7 +160,7 @@
         }
       },
       {
-        "name": "Score.onnx",
+        "name": "Score.output",
         "type": {
           "tensorType": {
             "elemType": 1,
diff --git a/test/BaselineOutput/Common/Onnx/Regression/Adult/SimplePipeline.txt b/test/BaselineOutput/Common/Onnx/Regression/Adult/SimplePipeline.txt
index 253b2ed684..f41b69f8c4 100644
--- a/test/BaselineOutput/Common/Onnx/Regression/Adult/SimplePipeline.txt
+++ b/test/BaselineOutput/Common/Onnx/Regression/Adult/SimplePipeline.txt
@@ -104,7 +104,7 @@
           "FeatureVector0"
         ],
         "output": [
-          "FeatureVector.onnx"
+          "FeatureVector.output"
         ],
         "name": "Identity",
         "opType": "Identity"
@@ -114,7 +114,7 @@
           "Target"
         ],
         "output": [
-          "Target.onnx"
+          "Target.output"
         ],
         "name": "Identity0",
         "opType": "Identity"
@@ -124,7 +124,7 @@
           "Score"
         ],
         "output": [
-          "Score.onnx"
+          "Score.output"
         ],
         "name": "Identity1",
         "opType": "Identity"
@@ -171,7 +171,7 @@
     ],
     "output": [
       {
-        "name": "FeatureVector.onnx",
+        "name": "FeatureVector.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -189,7 +189,7 @@
         }
       },
       {
-        "name": "Target.onnx",
+        "name": "Target.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -207,7 +207,7 @@
         }
       },
       {
-        "name": "Score.onnx",
+        "name": "Score.output",
         "type": {
           "tensorType": {
             "elemType": 1,
diff --git a/test/BaselineOutput/Common/Onnx/Transforms/IndicateMissingValues.txt b/test/BaselineOutput/Common/Onnx/Transforms/IndicateMissingValues.txt
index f13191abe4..ec818ec7c6 100644
--- a/test/BaselineOutput/Common/Onnx/Transforms/IndicateMissingValues.txt
+++ b/test/BaselineOutput/Common/Onnx/Transforms/IndicateMissingValues.txt
@@ -37,7 +37,7 @@
           "Features"
         ],
         "output": [
-          "Features.onnx"
+          "Features.output"
         ],
         "name": "Identity",
         "opType": "Identity"
@@ -47,7 +47,7 @@
           "MissingIndicator0"
         ],
         "output": [
-          "MissingIndicator.onnx"
+          "MissingIndicator.output"
         ],
         "name": "Identity0",
         "opType": "Identity"
@@ -76,7 +76,7 @@
     ],
     "output": [
       {
-        "name": "Features.onnx",
+        "name": "Features.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -94,7 +94,7 @@
         }
       },
       {
-        "name": "MissingIndicator.onnx",
+        "name": "MissingIndicator.output",
         "type": {
           "tensorType": {
             "elemType": 6,
diff --git a/test/BaselineOutput/Common/Onnx/Transforms/SelectColumns.txt b/test/BaselineOutput/Common/Onnx/Transforms/SelectColumns.txt
index aa5cc455dd..06351fe136 100644
--- a/test/BaselineOutput/Common/Onnx/Transforms/SelectColumns.txt
+++ b/test/BaselineOutput/Common/Onnx/Transforms/SelectColumns.txt
@@ -75,7 +75,7 @@
           "Size1"
         ],
         "output": [
-          "Size.onnx"
+          "Size.output"
         ],
         "name": "Identity3",
         "opType": "Identity"
@@ -85,7 +85,7 @@
           "Shape0"
         ],
         "output": [
-          "Shape.onnx"
+          "Shape.output"
         ],
         "name": "Identity4",
         "opType": "Identity"
@@ -95,7 +95,7 @@
           "Thickness0"
         ],
         "output": [
-          "Thickness.onnx"
+          "Thickness.output"
         ],
         "name": "Identity5",
         "opType": "Identity"
@@ -105,7 +105,7 @@
           "Label0"
         ],
         "output": [
-          "Label.onnx"
+          "Label.output"
         ],
         "name": "Identity6",
         "opType": "Identity"
@@ -278,7 +278,7 @@
     ],
     "output": [
       {
-        "name": "Size.onnx",
+        "name": "Size.output",
         "type": {
           "tensorType": {
             "elemType": 1,
@@ -296,7 +296,7 @@
         }
       },
       {
-        "name": "Shape.onnx",
+        "name": "Shape.output",
         "type": {
           "tensorType": {
             "elemType": 6,
@@ -314,7 +314,7 @@
         }
       },
       {
-        "name": "Thickness.onnx",
+        "name": "Thickness.output",
         "type": {
           "tensorType": {
             "elemType": 11,
@@ -332,7 +332,7 @@
         }
       },
       {
-        "name": "Label.onnx",
+        "name": "Label.output",
         "type": {
           "tensorType": {
             "elemType": 9,
diff --git a/test/BaselineOutput/Common/Onnx/Transforms/Sentiment/SmallWordEmbed.txt b/test/BaselineOutput/Common/Onnx/Transforms/Sentiment/SmallWordEmbed.txt
index 6d1786e598..9a116d6cf8 100644
--- a/test/BaselineOutput/Common/Onnx/Transforms/Sentiment/SmallWordEmbed.txt
+++ b/test/BaselineOutput/Common/Onnx/Transforms/Sentiment/SmallWordEmbed.txt
@@ -345,7 +345,7 @@
           "Tokens"
         ],
         "output": [
-          "Tokens.onnx"
+          "Tokens.output"
         ],
         "name": "Identity",
         "opType": "Identity"
@@ -355,7 +355,7 @@
           "Embed"
         ],
         "output": [
-          "Embed.onnx"
+          "Embed.output"
         ],
         "name": "Identity0",
         "opType": "Identity"
@@ -1053,7 +1053,7 @@
     ],
     "output": [
       {
-        "name": "Tokens.onnx",
+        "name": "Tokens.output",
         "type": {
           "tensorType": {
             "elemType": 8,
@@ -1071,7 +1071,7 @@
         }
       },
       {
-        "name": "Embed.onnx",
+        "name": "Embed.output",
         "type": {
           "tensorType": {
             "elemType": 1,
diff --git a/test/Microsoft.ML.Functional.Tests/ONNX.cs b/test/Microsoft.ML.Functional.Tests/ONNX.cs
index 245917a2f4..e9a20a4817 100644
--- a/test/Microsoft.ML.Functional.Tests/ONNX.cs
+++ b/test/Microsoft.ML.Functional.Tests/ONNX.cs
@@ -48,16 +48,11 @@ public void SaveOnnxModelLoadAndScoreFastTree()
                 mlContext.Model.ConvertToOnnx(model, data, file);
 
             // Load the model as a transform.
-            // Note that when saving an ML.NET model as an ONNX model, the column types and column names will
-            // change. The name changes as ONNX doesn't not allow the same name for an input and output within the ONNX model.
-            // Therefore names maintained but have a number appended to the end of the name. In this case, Score0 is the output
-            // of the ONNX model. We are renaming Score0 to Score using Copy Columns.
-            // ONNX also uses tensors and will return an output of a tensor with the dimension of [1,1] for a single float.
+            // ONNX uses tensors and will return an output of a tensor with the dimension of [1,1] for a single float.
             // Therefore the VectorScoreColumn class (which contains a float [] field called Score) is used for the return
             // type on the Prediction engine.
             // See #2980 and #2981 for more information.
-            var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(modelPath)
-                .Append(mlContext.Transforms.CopyColumns("Score", "Score.onnx"));
+            var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(modelPath);
             var onnxModel = onnxEstimator.Fit(data);
 
             // Create prediction engine and test predictions.
@@ -109,7 +104,7 @@ public void SaveOnnxModelLoadAndScoreKMeans()
             // TODO #2980: ONNX outputs don't match the outputs of the model, so we must hand-correct this for now.
             // TODO #2981: ONNX models cannot be fit as part of a pipeline, so we must use a workaround like this.
             var onnxWorkaroundPipeline = onnxModel.Append(
-                mlContext.Transforms.CopyColumns("Score", "Score.onnx").Fit(onnxModel.Transform(data)));
+                mlContext.Transforms.CopyColumns("Score", "Score").Fit(onnxModel.Transform(data)));
 
             // Create prediction engine and test predictions.
             var originalPredictionEngine = mlContext.Model.CreatePredictionEngine<HousingRegression, VectorScoreColumn>(model);
@@ -158,15 +153,10 @@ public void SaveOnnxModelLoadAndScoreSDCA()
             var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(modelPath);
             var onnxModel = onnxEstimator.Fit(data);
 
-            // TODO #2980: ONNX outputs don't match the outputs of the model, so we must hand-correct this for now.
-            // TODO #2981: ONNX models cannot be fit as part of a pipeline, so we must use a workaround like this.
-            var onnxWorkaroundPipeline = onnxModel.Append(
-                mlContext.Transforms.CopyColumns("Score", "Score.onnx").Fit(onnxModel.Transform(data)));
-
             // Create prediction engine and test predictions.
             var originalPredictionEngine = mlContext.Model.CreatePredictionEngine<HousingRegression, ScoreColumn>(model);
             // TODO #2982: ONNX produces vector types and not the original output type.
-            var onnxPredictionEngine = mlContext.Model.CreatePredictionEngine<HousingRegression, VectorScoreColumn>(onnxWorkaroundPipeline);
+            var onnxPredictionEngine = mlContext.Model.CreatePredictionEngine<HousingRegression, VectorScoreColumn>(onnxModel);
 
             // Take a handful of examples out of the dataset and compute predictions.
             var dataEnumerator = mlContext.Data.CreateEnumerable<HousingRegression>(mlContext.Data.TakeRows(data, 5), false);
diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs
index 52cd84c8f8..8d842a65fb 100644
--- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs
+++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs
@@ -85,17 +85,15 @@ public void SimpleEndToEndOnnxConversionTest()
             var onnxModelPath = GetOutputPath(onnxFileName);
             SaveOnnxModel(onnxModel, onnxModelPath, null);
 
-            if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows) && Environment.Is64BitProcess)
+            if (IsOnnxRuntimeSupported())
             {
                 // Step 3: Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(data);
                 var onnxResult = onnxTransformer.Transform(data);
 
                 // Step 4: Compare ONNX and ML.NET results.
-                CompareSelectedColumns<float>("Score", "Score.onnx", transformedData, onnxResult, 1);
+                CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult, 1);
             }
 
             // Step 5: Check ONNX model's text format. This test will be not necessary if Step 3 and Step 4 can run on Linux and
@@ -173,20 +171,18 @@ public void KmeansOnnxConversionTest()
 
             var onnxModel = mlContext.Model.ConvertToOnnxProtobuf(model, data);
 
+            var onnxFileName = "model.onnx";
+            var onnxModelPath = GetOutputPath(onnxFileName);
+            SaveOnnxModel(onnxModel, onnxModelPath, null);
+
             // Compare results produced by ML.NET and ONNX's runtime.
-            if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows) && Environment.Is64BitProcess)
+            if (IsOnnxRuntimeSupported())
             {
-                var onnxFileName = "model.onnx";
-                var onnxModelPath = GetOutputPath(onnxFileName);
-                SaveOnnxModel(onnxModel, onnxModelPath, null);
-
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(data);
                 var onnxResult = onnxTransformer.Transform(data);
-                CompareSelectedColumns<float>("Score", "Score.onnx", transformedData, onnxResult, 3);
+                CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult, 3);
             }
 
             // Check ONNX model's text format. We save the produced ONNX model as a text file and compare it against
@@ -229,22 +225,21 @@ public void RegressionTrainersOnnxConversionTest()
                 var model = estimator.Fit(dataView);
                 var transformedData = model.Transform(dataView);
                 var onnxModel = mlContext.Model.ConvertToOnnxProtobuf(model, dataView);
+
                 // Compare model scores produced by ML.NET and ONNX's runtime
                 if (IsOnnxRuntimeSupported())
                 {
                     var onnxFileName = $"{estimator.ToString()}.onnx";
                     var onnxModelPath = GetOutputPath(onnxFileName);
                     SaveOnnxModel(onnxModel, onnxModelPath, null);
-                    // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                    string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                    string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
-                    CompareSelectedColumns<float>(transformedData.Schema[2].Name, outputNames[2], transformedData, onnxResult, 3); // compare score results 
+                    CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult, 3);
                 }
                 // Compare the Onnx graph to a baseline if OnnxRuntime is not supported
-                else
+                //else
                 {
                     var onnxFileName = $"{estimator.ToString()}.txt";
                     var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "Regression", "Adult");
@@ -290,20 +285,20 @@ public void BinaryClassificationTrainersOnnxConversionTest()
                 var model = pipeline.Fit(dataView);
                 var transformedData = model.Transform(dataView);
                 var onnxModel = mlContext.Model.ConvertToOnnxProtobuf(model, dataView);
+
+                var onnxFileName = $"{estimator.ToString()}.onnx";
+                var onnxModelPath = GetOutputPath(onnxFileName);
+                SaveOnnxModel(onnxModel, onnxModelPath, null);
+
                 // Compare model scores produced by ML.NET and ONNX's runtime.
                 if (IsOnnxRuntimeSupported())
                 {
-                    var onnxFileName = $"{estimator.ToString()}.onnx";
-                    var onnxModelPath = GetOutputPath(onnxFileName);
-                    SaveOnnxModel(onnxModel, onnxModelPath, null);
                     // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                    string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                    string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
-                    CompareSelectedColumns<float>(transformedData.Schema[5].Name, outputNames[3], transformedData, onnxResult, 3); //compare scores
-                    CompareSelectedColumns<bool>(transformedData.Schema[4].Name, outputNames[2], transformedData, onnxResult); //compare predicted labels
+                    CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult, 3); //compare scores
+                    CompareSelectedColumns<bool>("PredictedLabel", "PredictedLabel", transformedData, onnxResult); //compare predicted labels
                 }
             }
             Done();
@@ -326,19 +321,17 @@ public void TestVectorWhiteningOnnxConversionTest()
             var onnxModel = mlContext.Model.ConvertToOnnxProtobuf(model, dataView);
 
             // Compare model scores produced by ML.NET and ONNX's runtime.
+            var onnxFileName = $"VectorWhitening.onnx";
+            var onnxModelPath = GetOutputPath(onnxFileName);
+            SaveOnnxModel(onnxModel, onnxModelPath, null);
+
             if (IsOnnxRuntimeSupported())
             {
-                var onnxFileName = $"VectorWhitening.onnx";
-                var onnxModelPath = GetOutputPath(onnxFileName);
-                SaveOnnxModel(onnxModel, onnxModelPath, null);
-                // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
-                CompareSelectedColumns<float>(transformedData.Schema[2].Name, outputNames[2], transformedData, onnxResult); // whitened1
-                CompareSelectedColumns<float>(transformedData.Schema[3].Name, outputNames[3], transformedData, onnxResult); // whitened2
+                CompareSelectedColumns<float>("whitened1", "whitened1", transformedData, onnxResult);
+                CompareSelectedColumns<float>("whitened2", "whitened2", transformedData, onnxResult);
             }
             Done();
         }
@@ -381,21 +374,20 @@ public void PlattCalibratorOnnxConversionTest()
                 var transformedData = model.Transform(dataView);
                 var onnxModel = mlContext.Model.ConvertToOnnxProtobuf(model, dataView);
 
+                var onnxFileName = $"{estimator.ToString()}-WithPlattCalibrator.onnx";
+                var onnxModelPath = GetOutputPath(onnxFileName);
+                SaveOnnxModel(onnxModel, onnxModelPath, null);
+
                 // Compare model scores produced by ML.NET and ONNX's runtime. 
                 if (IsOnnxRuntimeSupported())
                 {
-                    var onnxFileName = $"{estimator.ToString()}-WithPlattCalibrator.onnx";
-                    var onnxModelPath = GetOutputPath(onnxFileName);
-                    SaveOnnxModel(onnxModel, onnxModelPath, null);
                     // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                    string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                    string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
-                    CompareSelectedColumns<float>(transformedData.Schema[5].Name, outputNames[3], transformedData, onnxResult, 3); //compare scores
-                    CompareSelectedColumns<bool>(transformedData.Schema[4].Name, outputNames[2], transformedData, onnxResult); //compare predicted labels
-                    CompareSelectedColumns<float>(transformedData.Schema.Last().Name, outputNames.Last(), transformedData, onnxResult, 3); //compare probabilities
+                    CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult, 3);
+                    CompareSelectedColumns<bool>("PredictedLabel", "PredictedLabel", transformedData, onnxResult);
+                    CompareSelectedColumns<float>("Probability", "Probability", transformedData, onnxResult, 3);
                 }
             }
             Done();
@@ -430,20 +422,17 @@ public void PlattCalibratorOnnxConversionTest2()
             var transformedData = calibratorTransformer.Transform(data);
             var onnxModel = mlContext.Model.ConvertToOnnxProtobuf(calibratorTransformer, data);
 
+            var onnxFileName = $"{calibratorTransformer.ToString()}.onnx";
+            var onnxModelPath = GetOutputPath(onnxFileName);
+            SaveOnnxModel(onnxModel, onnxModelPath, null);
+
             // Compare model scores produced by ML.NET and ONNX's runtime.
             if (IsOnnxRuntimeSupported())
             {
-                var onnxFileName = $"{calibratorTransformer.ToString()}.onnx";
-                var onnxModelPath = GetOutputPath(onnxFileName);
-                SaveOnnxModel(onnxModel, onnxModelPath, null);
-
-                // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(data);
                 var onnxResult = onnxTransformer.Transform(data);
-                CompareSelectedColumns<float>(transformedData.Schema.Last().Name, outputNames.Last(), transformedData, onnxResult, 3); //compare probabilities
+                CompareSelectedColumns<float>("Probability", "Probability", transformedData, onnxResult, 3); //compare probabilities
             }
             Done();
         }
@@ -464,22 +453,21 @@ public void TextNormalizingOnnxConversionTest()
             var transformedData = model.Transform(dataView);
             var onnxModel = mlContext.Model.ConvertToOnnxProtobuf(model, dataView);
 
+            var onnxFileName = $"TextNormalizing.onnx";
+            var onnxModelPath = GetOutputPath(onnxFileName);
+            SaveOnnxModel(onnxModel, onnxModelPath, null);
+
             // Compare model scores produced by ML.NET and ONNX's runtime.
             // Skipping test in Linux platforms temporarily
             if (IsOnnxRuntimeSupported() && !RuntimeInformation.IsOSPlatform(OSPlatform.Linux))
             {
-                var onnxFileName = $"TextNormalizing.onnx";
-                var onnxModelPath = GetOutputPath(onnxFileName);
-                SaveOnnxModel(onnxModel, onnxModelPath, null);
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
-                CompareSelectedColumns<ReadOnlyMemory<char>>(transformedData.Schema[2].Name, outputNames[2], transformedData, onnxResult); //compare NormText
-                CompareSelectedColumns<ReadOnlyMemory<char>>(transformedData.Schema[3].Name, outputNames[3], transformedData, onnxResult); //compare UpperText
-                CompareSelectedColumns<ReadOnlyMemory<char>>(transformedData.Schema[4].Name, outputNames[4], transformedData, onnxResult); //compare OriginalText
+                CompareSelectedColumns<ReadOnlyMemory<char>>("NormText", "NormText", transformedData, onnxResult);
+                CompareSelectedColumns<ReadOnlyMemory<char>>("UpperText", "UpperText", transformedData, onnxResult);
+                CompareSelectedColumns<ReadOnlyMemory<char>>("OriginalText", "OriginalText", transformedData, onnxResult);
             }
             Done();
         }
@@ -520,15 +508,13 @@ public void LpNormOnnxConversionTest(
             SaveOnnxModel(onnxModel, onnxModelPath, null);
 
             // Compare results produced by ML.NET and ONNX's runtime.
-            if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows) && Environment.Is64BitProcess)
+            if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
-                CompareSelectedColumns<float>(nameof(DataPoint.Features), outputNames[0], transformedData, onnxResult, 3);
+                CompareSelectedColumns<float>("Features", "Features", transformedData, onnxResult, 3);
             }
 
             Done();
@@ -969,19 +955,19 @@ public void TokenizingByCharactersOnnxConversionTest(bool useMarkerCharacters)
             var model = pipeline.Fit(dataView);
             var transformedData = model.Transform(dataView);
             var onnxModel = mlContext.Model.ConvertToOnnxProtobuf(model, dataView);
+            
             // Compare model scores produced by ML.NET and ONNX's runtime. 
+            var onnxFileName = $"TokenizingByCharacters.onnx";
+            var onnxModelPath = GetOutputPath(onnxFileName);
+            SaveOnnxModel(onnxModel, onnxModelPath, null);
+
             if (IsOnnxRuntimeSupported())
             {
-                var onnxFileName = $"TokenizingByCharacters.onnx";
-                var onnxModelPath = GetOutputPath(onnxFileName);
-                SaveOnnxModel(onnxModel, onnxModelPath, null);
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
-                CompareSelectedColumns<ushort>(transformedData.Schema[2].Name, outputNames[2], transformedData, onnxResult); //compare scores
+                CompareSelectedColumns<ushort>("TokenizedText", "TokenizedText", transformedData, onnxResult);
             }
             Done();
         }
@@ -1051,15 +1037,13 @@ public void OnnxTypeConversionTest(DataKind fromKind, DataKind toKind)
             var onnxModelPath = GetOutputPath(onnxFileName);
             SaveOnnxModel(onnxModel, onnxModelPath, null);
 
-            if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows) && Environment.Is64BitProcess)
+            if (IsOnnxRuntimeSupported())
             {
-                string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
 
-                CompareResults(model.ColumnPairs[0].outputColumnName, outputNames[1], mlnetResult, onnxResult);
+                CompareResults("ValueConverted", "ValueConverted", mlnetResult, onnxResult);
             }
 
             Done();
@@ -1089,15 +1073,13 @@ public void PcaOnnxConversionTest()
 
                 SaveOnnxModel(onnxModel, onnxModelPath, null);
 
-                if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows) && Environment.Is64BitProcess)
+                if (IsOnnxRuntimeSupported())
                 {
                     // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                    string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                    string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
-                    CompareSelectedColumns<float>(model.ColumnPairs[0].outputColumnName, outputNames[2], transformedData, onnxResult);
+                    CompareSelectedColumns<float>("pca", "pca", transformedData, onnxResult);
                 }
             }
 
@@ -1153,12 +1135,10 @@ public void IndicateMissingValuesOnnxConversionTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
-                CompareSelectedColumns<int>(model.LastTransformer.ColumnPairs[0].outputColumnName, outputNames[1], transformedData, onnxResult);
+                CompareSelectedColumns<int>("MissingIndicator", "MissingIndicator", transformedData, onnxResult);
             }
 
             CheckEquality(subDir, onnxTextName, parseOption: NumberParseOption.UseSingle);
@@ -1193,14 +1173,12 @@ public void ValueToKeyandKeyToValueMappingOnnxConversionTest(DataKind valueType)
 
             if (IsOnnxRuntimeSupported())
             {
-                string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
 
-                CompareResults(mlnetResult.Schema[2].Name, outputNames[2], mlnetResult, onnxResult); //compare output values
-                CompareSelectedColumns<uint>(mlnetResult.Schema[1].Name, outputNames[1], mlnetResult, onnxResult); //compare keys
+                CompareResults("ValueOutput", "ValueOutput", mlnetResult, onnxResult);
+                CompareSelectedColumns<uint>("Key", "Key", mlnetResult, onnxResult);
             }
             Done();
         }
@@ -1233,15 +1211,14 @@ public void WordTokenizerOnnxConversionTest()
             var onnxFilename = "Tokenizer.onnx";
             var onnxFilePath = GetOutputPath(onnxFilename);
             SaveOnnxModel(onnxModel, onnxFilePath, null);
+
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxFilePath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxFilePath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
-                CompareSelectedColumns<ReadOnlyMemory<char>>(transformedData.Schema[1].Name, outputNames[1], transformedData, onnxResult);
+                CompareSelectedColumns<ReadOnlyMemory<char>>("Tokens", "Tokens", transformedData, onnxResult);
             }
 
             Done();
@@ -1297,15 +1274,14 @@ public void NgramOnnxConversionTest(
                 var onnxFilename = $"Ngram-{i}-{ngramLength}-{useAllLength}-{weighting}.onnx";
                 var onnxFilePath = GetOutputPath(onnxFilename);
                 SaveOnnxModel(onnxModel, onnxFilePath, null);
-                if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows) && Environment.Is64BitProcess)
+
+                if (IsOnnxRuntimeSupported())
                 {
-                    // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                    string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                    string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxFilePath);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxFilePath);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
-                    CompareSelectedColumns<float>(transformedData.Schema[transformedData.Schema.Count-1].Name, outputNames[outputNames.Length-1], transformedData, onnxResult, 3); //comparing Ngrams
+                    var columnName = i == pipelines.Length - 1 ? "Tokens" : "NGrams";
+                    CompareSelectedColumns<float>(columnName, columnName, transformedData, onnxResult, 3);
                 }
             }
             Done();
@@ -1362,7 +1338,7 @@ public void OptionalColumnOnnxTest(DataKind dataKind)
                 var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
-                CompareResults("Label", "Label.onnx", outputData, onnxResult);
+                CompareResults("Label", "Label", outputData, onnxResult);
             }
             Done();
         }
@@ -1392,12 +1368,10 @@ public void KeyToValueOnnxConversionTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
-                CompareSelectedColumns<ReadOnlyMemory<Char>>(transformedData.Schema[3].Name, outputNames[3], transformedData, onnxResult);
+                CompareSelectedColumns<ReadOnlyMemory<Char>>("LabelValue", "LabelValue", transformedData, onnxResult);
             }
 
             Done();
@@ -1462,13 +1436,11 @@ public void MulticlassTrainersOnnxConversionTest()
                 if (IsOnnxRuntimeSupported())
                 {
                     // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                    string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                    string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
-                    CompareSelectedColumns<uint>(transformedData.Schema[5].Name, outputNames[2], transformedData, onnxResult); //compare predicted labels
-                    CompareSelectedColumns<float>(transformedData.Schema[6].Name, outputNames[3], transformedData, onnxResult, 4); //compare scores
+                    CompareSelectedColumns<uint>("PredictedLabel", "PredictedLabel", transformedData, onnxResult);
+                    CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult, 4);
                 }
             }
             Done();
@@ -1497,12 +1469,10 @@ public void CopyColumnsOnnxTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
-                CompareSelectedColumns<float>(model.ColumnPairs[0].outputColumnName, outputNames[2], transformedData, onnxResult);
+                CompareSelectedColumns<float>("Target", "Target1", transformedData, onnxResult);
             }
             Done();
         }
@@ -1575,8 +1545,13 @@ public void UseKeyDataViewTypeAsUInt32InOnnxInput()
                 var onnxEstimator2 = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath2);
                 var onnxResult2 = onnxEstimator2.Fit(originalData).Transform(originalData);
 
+                var stdSuffix = ".output";
                 foreach (var name in outputNames)
-                    CompareResults(name, name, onnxResult, onnxResult2);
+                {
+                    Assert.EndsWith(stdSuffix, name);
+                    var colName = name.Replace(stdSuffix, "");
+                    CompareResults(colName, colName, onnxResult, onnxResult2);
+                }
             }
 
             Done();
@@ -1620,15 +1595,13 @@ public void FeatureSelectionOnnxTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
-                CompareSelectedColumns<float>("FeatureSelectMIScalarFloat", "FeatureSelectMIScalarFloat.onnx", transformedData, onnxResult);
-                CompareSelectedColumns<float>("FeatureSelectMIVectorFloat", "FeatureSelectMIVectorFloat.onnx", transformedData, onnxResult);
-                CompareSelectedColumns<float>("ScalFeatureSelectMissing690", "ScalFeatureSelectMissing690.onnx", transformedData, onnxResult);
-                CompareSelectedColumns<double>("VecFeatureSelectMissing690", "VecFeatureSelectMissing690.onnx", transformedData, onnxResult);
+                CompareSelectedColumns<float>("FeatureSelectMIScalarFloat", "FeatureSelectMIScalarFloat", transformedData, onnxResult);
+                CompareSelectedColumns<float>("FeatureSelectMIVectorFloat", "FeatureSelectMIVectorFloat", transformedData, onnxResult);
+                CompareSelectedColumns<float>("ScalFeatureSelectMissing690", "ScalFeatureSelectMissing690", transformedData, onnxResult);
+                CompareSelectedColumns<double>("VecFeatureSelectMissing690", "VecFeatureSelectMissing690", transformedData, onnxResult);
             }
             Done();
         }
@@ -1676,15 +1649,15 @@ public void SelectColumnsOnnxTest()
 
                 // Verify that onnx output has only the four columns we selected from the input
                 Assert.Equal(4, outputNames.Length);
-                Assert.Equal("Size.onnx", outputNames[0]);
-                Assert.Equal("Shape.onnx", outputNames[1]);
-                Assert.Equal("Thickness.onnx", outputNames[2]);
-                Assert.Equal("Label.onnx", outputNames[3]);
-
-                CompareSelectedColumns<Single>("Size", "Size.onnx", transformedData, onnxResult);
-                CompareSelectedColumns<int>("Shape", "Shape.onnx", transformedData, onnxResult);
-                CompareSelectedColumns<double>("Thickness", "Thickness.onnx", transformedData, onnxResult);
-                CompareSelectedColumns<bool>("Label", "Label.onnx", transformedData, onnxResult);
+                Assert.Equal("Size.output", outputNames[0]);
+                Assert.Equal("Shape.output", outputNames[1]);
+                Assert.Equal("Thickness.output", outputNames[2]);
+                Assert.Equal("Label.output", outputNames[3]);
+
+                CompareSelectedColumns<Single>("Size", "Size", transformedData, onnxResult);
+                CompareSelectedColumns<int>("Shape", "Shape", transformedData, onnxResult);
+                CompareSelectedColumns<double>("Thickness", "Thickness", transformedData, onnxResult);
+                CompareSelectedColumns<bool>("Label", "Label", transformedData, onnxResult);
             }
 
             onnxFileName = "SelectColumns.txt";