diff --git a/src/Microsoft.ML.Data/Prediction/Calibrator.cs b/src/Microsoft.ML.Data/Prediction/Calibrator.cs
index efa52d2ff6..487726572b 100644
--- a/src/Microsoft.ML.Data/Prediction/Calibrator.cs
+++ b/src/Microsoft.ML.Data/Prediction/Calibrator.cs
@@ -746,13 +746,10 @@ private static bool NeedCalibration(IHostEnvironment env, IChannel ch, ICalibrat
         /// <param name="trainer">The trainer used to train the predictor.</param>
         /// <param name="predictor">The predictor that needs calibration.</param>
         /// <param name="data">The examples to used for calibrator training.</param>
-        /// <param name="needValueMapper">Indicates whether the predictor returned needs to be an <see cref="IValueMapper"/>.
-        /// This parameter is needed for OVA that uses the predictors as <see cref="IValueMapper"/>s. If it is false,
-        /// The predictor returned is an an <see cref="ISchemaBindableMapper"/>.</param>
         /// <returns>The original predictor, if no calibration is needed, 
         /// or a metapredictor that wraps the original predictor and the newly trained calibrator.</returns>
         public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel ch, ICalibratorTrainer calibrator,
-            int maxRows, ITrainer trainer, IPredictor predictor, RoleMappedData data, bool needValueMapper = false)
+            int maxRows, ITrainer trainer, IPredictor predictor, RoleMappedData data)
         {
             Contracts.CheckValue(env, nameof(env));
             env.CheckValue(ch, nameof(ch));
@@ -763,7 +760,7 @@ public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel
             if (!NeedCalibration(env, ch, calibrator, trainer, predictor, data.Schema))
                 return predictor;
 
-            return TrainCalibrator(env, ch, calibrator, maxRows, predictor, data, needValueMapper);
+            return TrainCalibrator(env, ch, calibrator, maxRows, predictor, data);
         }
 
         /// <summary>
@@ -775,13 +772,10 @@ public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel
         /// <param name="maxRows">The maximum rows to use for calibrator training.</param>
         /// <param name="predictor">The predictor that needs calibration.</param>
         /// <param name="data">The examples to used for calibrator training.</param>
-        /// <param name="needValueMapper">Indicates whether the predictor returned needs to be an <see cref="IValueMapper"/>.
-        /// This parameter is needed for OVA that uses the predictors as <see cref="IValueMapper"/>s. If it is false,
-        /// The predictor returned is an an <see cref="ISchemaBindableMapper"/>.</param>
         /// <returns>The original predictor, if no calibration is needed, 
         /// or a metapredictor that wraps the original predictor and the newly trained calibrator.</returns>
         public static IPredictor TrainCalibrator(IHostEnvironment env, IChannel ch, ICalibratorTrainer caliTrainer,
-            int maxRows, IPredictor predictor, RoleMappedData data, bool needValueMapper = false)
+            int maxRows, IPredictor predictor, RoleMappedData data)
         {
             Contracts.CheckValue(env, nameof(env));
             env.CheckValue(ch, nameof(ch));
@@ -834,10 +828,10 @@ public static IPredictor TrainCalibrator(IHostEnvironment env, IChannel ch, ICal
                 }
             }
             var cali = caliTrainer.FinishTraining(ch);
-            return CreateCalibratedPredictor(env, (IPredictorProducing<Float>)predictor, cali, needValueMapper);
+            return CreateCalibratedPredictor(env, (IPredictorProducing<Float>)predictor, cali);
         }
 
-        public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironment env, IPredictorProducing<Float> predictor, ICalibrator cali, bool needValueMapper = false)
+        public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironment env, IPredictorProducing<Float> predictor, ICalibrator cali)
         {
             Contracts.Assert(predictor != null);
             if (cali == null)
@@ -853,7 +847,7 @@ public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironm
             var predWithFeatureScores = predictor as IPredictorWithFeatureWeights<Float>;
             if (predWithFeatureScores != null && predictor is IParameterMixer<Float> && cali is IParameterMixer)
                 return new ParameterMixingCalibratedPredictor(env, predWithFeatureScores, cali);
-            if (needValueMapper)
+            if (predictor is IValueMapper)
                 return new CalibratedPredictor(env, predictor, cali);
             return new SchemaBindableCalibratedPredictor(env, predictor, cali);
         }
diff --git a/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs b/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs
index 3d6e1e67b2..8aa6e4b6e0 100644
--- a/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs
+++ b/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs
@@ -92,7 +92,7 @@ private TScalarPredictor TrainOne(IChannel ch, TScalarTrainer trainer, RoleMappe
                 else
                     calibrator = Args.Calibrator.CreateInstance(Host);
                 var res = CalibratorUtils.TrainCalibratorIfNeeded(Host, ch, calibrator, Args.MaxCalibrationExamples,
-                    trainer, predictor, td, true);
+                    trainer, predictor, td);
                 predictor = res as TScalarPredictor;
                 Host.Check(predictor != null, "Calibrated predictor does not implement the expected interface");
             }
diff --git a/test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs b/test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs
index b42dee2d52..f8ac506f6e 100644
--- a/test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs
+++ b/test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs
@@ -798,5 +798,64 @@ public void TestOvaMacro()
                 }
             }
         }
+
+        [Fact]
+        public void TestOvaMacroWithUncalibratedLearner()
+        {
+            var dataPath = GetDataPath(@"iris.txt");
+            using (var env = new TlcEnvironment(42))
+            {
+                // Specify subgraph for OVA
+                var subGraph = env.CreateExperiment();
+                var learnerInput = new Trainers.AveragedPerceptronBinaryClassifier { Shuffle = false };
+                var learnerOutput = subGraph.Add(learnerInput);
+                // Create pipeline with OVA and multiclass scoring.
+                var experiment = env.CreateExperiment();
+                var importInput = new ML.Data.TextLoader(dataPath);
+                importInput.Arguments.Column = new TextLoaderColumn[]
+                {
+                    new TextLoaderColumn { Name = "Label", Source = new[] { new TextLoaderRange(0) } },
+                    new TextLoaderColumn { Name = "Features", Source = new[] { new TextLoaderRange(1,4) } }
+                };
+                var importOutput = experiment.Add(importInput);
+                var oneVersusAll = new Models.OneVersusAll
+                {
+                    TrainingData = importOutput.Data,
+                    Nodes = subGraph,
+                    UseProbabilities = true,
+                };
+                var ovaOutput = experiment.Add(oneVersusAll);
+                var scoreInput = new ML.Transforms.DatasetScorer
+                {
+                    Data = importOutput.Data,
+                    PredictorModel = ovaOutput.PredictorModel
+                };
+                var scoreOutput = experiment.Add(scoreInput);
+                var evalInput = new ML.Models.ClassificationEvaluator
+                {
+                    Data = scoreOutput.ScoredData
+                };
+                var evalOutput = experiment.Add(evalInput);
+                experiment.Compile();
+                experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false));
+                experiment.Run();
+
+                var data = experiment.GetOutput(evalOutput.OverallMetrics);
+                var schema = data.Schema;
+                var b = schema.TryGetColumnIndex(MultiClassClassifierEvaluator.AccuracyMacro, out int accCol);
+                Assert.True(b);
+                using (var cursor = data.GetRowCursor(col => col == accCol))
+                {
+                    var getter = cursor.GetGetter<double>(accCol);
+                    b = cursor.MoveNext();
+                    Assert.True(b);
+                    double acc = 0;
+                    getter(ref acc);
+                    Assert.Equal(0.71, acc, 2);
+                    b = cursor.MoveNext();
+                    Assert.False(b);
+                }
+            }
+        }
     }
 }