Added OneVersusAll and PairwiseCoupling samples.

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/MulticlassClassification.ttinclude

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+using System;
+using System.Collections.Generic;
+using System.Linq;
+using Microsoft.ML.Data;
+<# if (TrainerOptions != null) { #>
+<#=OptionsInclude#>
+<# } #>
+
+namespace Microsoft.ML.Samples.Dynamic.Trainers.MulticlassClassification
+{
+    public static class <#=ClassName#>
+    {<#=Comments#>
+        public static void Example()
+        {
+            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
+            // as a catalog of available operations and as the source of randomness.
+            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            var mlContext = new MLContext(seed: 0);
+
+            // Create a list of training data points.
+            var dataPoints = GenerateRandomDataPoints(1000);
+
+            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
+
+<# if (MetaTrainer != null) { #>
+            // Define the trainer.
+            var pipeline =
+                    // Convert the string labels into key types.
+                    mlContext.Transforms.Conversion.MapValueToKey("Label")
+                    // Apply <#=MetaTrainer#> multiclass meta trainer on top of binary trainer.
+                    .Append(mlContext.MulticlassClassification.Trainers.<#=MetaTrainer#>(<#=Trainer#>()));
+<# } else if (TrainerOptions == null) { #>
+            // Define the trainer.
+            var pipeline =
+                    // Convert the string labels into key types.
+                    mlContext.Transforms.Conversion.MapValueToKey("Label")
+                    // Apply <#=Trainer#> multiclass trainer.
+                    .Append(mlContext.MulticlassClassification.Trainers.<#=Trainer#>());
+<# } else { #>
+            // Define trainer options.
+            var options = new <#=TrainerOptions#>;
+
+            // Define the trainer.
+            var pipeline = mlContext.MulticlassClassification.Trainers.<#=Trainer#>(options);
+<# } #>
+
+            // Train the model.
+            var model = pipeline.Fit(trainingData);
+
+            // Create testing data. Use different random seed to make it different from training data.
+            var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
+
+            // Run the model on test data set.
+            var transformedTestData = model.Transform(testData);
+
+            // Convert IDataView object to a list.
+            var predictions = mlContext.Data.CreateEnumerable<Prediction>(transformedTestData, reuseRowObject: false).ToList();
+
+            // Look at 5 predictions
+            foreach (var p in predictions.Take(5))
+                Console.WriteLine($"Label: {p.Label}, Prediction: {p.PredictedLabel}");
+
+            // Evaluate the overall metrics
+            var metrics = mlContext.MulticlassClassification.Evaluate(transformedTestData);
+            SamplesUtils.ConsoleUtils.PrintMetrics(metrics);
+
+            <#=ExpectedOutput#>
+        }
+
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        {
+            var random = new Random(seed);
+            float randomFloat() => (float)random.NextDouble();
+            for (int i = 0; i < count; i++)
+            {
+                // Generate Labels that are integers 0, 1 or 2
+                var label = random.Next(3);
+                yield return new DataPoint
+                {
+                    Label = (uint)label,
+                    // Create random features that are correlated with the label.
+                    // The feature values are slightly increased by adding a constant multiple of label.
+                    Features = Enumerable.Repeat(label, 20).Select(x => randomFloat() + label * 0.2f).ToArray()
+                };
+            }
+        }
+
+        // Example with label and 20 feature values. A data set is a collection of such examples.
+        private class DataPoint
+        {
+            public uint Label { get; set; }
+            [VectorType(20)]
+            public float[] Features { get; set; }
+        }
+
+        // Class used to capture predictions.
+        private class Prediction
+        {
+            // Original label.
+            public uint Label { get; set; }
+            // Predicted label from the trainer.
+            public uint PredictedLabel { get; set; }
+        }
+    }
+}

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/NaiveBayes.cs

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+using System;
+using System.Collections.Generic;
+using System.Linq;
+using Microsoft.ML.Data;
+
+namespace Microsoft.ML.Samples.Dynamic.Trainers.MulticlassClassification
+{
+    public static class NaiveBayes
+    {
+        public static void Example()
+        {
+            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
+            // as a catalog of available operations and as the source of randomness.
+            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            var mlContext = new MLContext(seed: 0);
+
+            // Create a list of training data points.
+            var dataPoints = GenerateRandomDataPoints(1000);
+
+            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
+
+            // Define the trainer.
+            var pipeline =
+                    // Convert the string labels into key types.
+                    mlContext.Transforms.Conversion.MapValueToKey("Label")
+                    // Apply NaiveBayes multiclass trainer.
+                    .Append(mlContext.MulticlassClassification.Trainers.NaiveBayes());
+
+            // Train the model.
+            var model = pipeline.Fit(trainingData);
+
+            // Create testing data. Use different random seed to make it different from training data.
+            var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
+
+            // Run the model on test data set.
+            var transformedTestData = model.Transform(testData);
+
+            // Convert IDataView object to a list.
+            var predictions = mlContext.Data.CreateEnumerable<Prediction>(transformedTestData, reuseRowObject: false).ToList();
+
+            // Look at 5 predictions
+            foreach (var p in predictions.Take(5))
+                Console.WriteLine($"Label: {p.Label}, Prediction: {p.PredictedLabel}");
+
+            // Evaluate the overall metrics
+            var metrics = mlContext.MulticlassClassification.Evaluate(transformedTestData);
+            SamplesUtils.ConsoleUtils.PrintMetrics(metrics);
+
+            // Expected output:
+            // Expected output:
+            // Micro Accuracy: 0.35
+            // Macro Accuracy: 0.33
+            // Log Loss: 34.54
+            // Log Loss Reduction: -30.47
+        }
+
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        {
+            var random = new Random(seed);
+            float randomFloat() => (float)random.NextDouble();
+            for (int i = 0; i < count; i++)
+            {
+                // Generate Labels that are integers 0, 1 or 2
+                var label = random.Next(3);
+                yield return new DataPoint
+                {
+                    Label = (uint)label,
+                    // Create random features that are correlated with the label.
+                    // The feature values are slightly increased by adding a constant multiple of label.
+                    Features = Enumerable.Repeat(label, 20).Select(x => randomFloat() + label * 0.2f).ToArray()
+                };
+            }
+        }
+
+        // Example with label and 20 feature values. A data set is a collection of such examples.
+        private class DataPoint
+        {
+            public uint Label { get; set; }
+            [VectorType(20)]
+            public float[] Features { get; set; }
+        }
+
+        // Class used to capture predictions.
+        private class Prediction
+        {
+            // Original label.
+            public uint Label { get; set; }
+            // Predicted label from the trainer.
+            public uint PredictedLabel { get; set; }
+        }
+    }
+}

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/NaiveBayes.tt

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+<#@ include file="MulticlassClassification.ttinclude"#>
+<#+
+string ClassName="NaiveBayes";
+string Trainer = "NaiveBayes";
+string MetaTrainer = null;
+string TrainerOptions = null;
+
+string OptionsInclude = "";
+string Comments= "";
+
+string ExpectedOutputPerInstance= @"// Expected output:
+            //   Label: 1, Prediction: 2
+            //   Label: 2, Prediction: 2
+            //   Label: 3, Prediction: 2
+            //   Label: 2, Prediction: 2
+            //   Label: 3, Prediction: 2";
+
+string ExpectedOutput = @"// Expected output:
+            // Expected output:
+            // Micro Accuracy: 0.35
+            // Macro Accuracy: 0.33
+            // Log Loss: 34.54
+            // Log Loss Reduction: -30.47";
+#>

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/OneVersusAll.cs

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+using System;
+using System.Collections.Generic;
+using System.Linq;
+using Microsoft.ML.Data;
+
+namespace Microsoft.ML.Samples.Dynamic.Trainers.MulticlassClassification
+{
+    public static class OneVersusAll
+    {
+        public static void Example()
+        {
+            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
+            // as a catalog of available operations and as the source of randomness.
+            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            var mlContext = new MLContext(seed: 0);
+
+            // Create a list of training data points.
+            var dataPoints = GenerateRandomDataPoints(1000);
+
+            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
+
+            // Define the trainer.
+            var pipeline =
+                    // Convert the string labels into key types.
+                    mlContext.Transforms.Conversion.MapValueToKey("Label")
+                    // Apply OneVersusAll multiclass meta trainer on top of binary trainer.
+                    .Append(mlContext.MulticlassClassification.Trainers.OneVersusAll(mlContext.BinaryClassification.Trainers.SdcaLogisticRegression()));
+
+            // Train the model.
+            var model = pipeline.Fit(trainingData);
+
+            // Create testing data. Use different random seed to make it different from training data.
+            var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
+
+            // Run the model on test data set.
+            var transformedTestData = model.Transform(testData);
+
+            // Convert IDataView object to a list.
+            var predictions = mlContext.Data.CreateEnumerable<Prediction>(transformedTestData, reuseRowObject: false).ToList();
+
+            // Look at 5 predictions
+            foreach (var p in predictions.Take(5))
+                Console.WriteLine($"Label: {p.Label}, Prediction: {p.PredictedLabel}");
+
+            // Evaluate the overall metrics
+            var metrics = mlContext.MulticlassClassification.Evaluate(transformedTestData);
+            SamplesUtils.ConsoleUtils.PrintMetrics(metrics);
+
+            // Expected output:
+            // Expected output:
+            // Micro Accuracy: 0.90
+            // Macro Accuracy: 0.90
+            // Log Loss: 0.37
+            // Log Loss Reduction: 0.67
+        }
+
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        {
+            var random = new Random(seed);
+            float randomFloat() => (float)random.NextDouble();
+            for (int i = 0; i < count; i++)
+            {
+                // Generate Labels that are integers 0, 1 or 2
+                var label = random.Next(3);
+                yield return new DataPoint
+                {
+                    Label = (uint)label,
+                    // Create random features that are correlated with the label.
+                    // The feature values are slightly increased by adding a constant multiple of label.
+                    Features = Enumerable.Repeat(label, 20).Select(x => randomFloat() + label * 0.2f).ToArray()
+                };
+            }
+        }
+
+        // Example with label and 50 feature values. A data set is a collection of such examples.
+        private class DataPoint
+        {
+            public uint Label { get; set; }
+            [VectorType(20)]
+            public float[] Features { get; set; }
+        }
+
+        // Class used to capture predictions.
+        private class Prediction
+        {
+            // Original label.
+            public uint Label { get; set; }
+            // Predicted label from the trainer.
+            public uint PredictedLabel { get; set; }
+        }
+    }
+}

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/OneVersusAll.tt

@@ -0,0 +1,24 @@
+<#@ include file="MulticlassClassification.ttinclude"#>
+<#+
+string ClassName="OneVersusAll";
+string Trainer = "mlContext.BinaryClassification.Trainers.SdcaLogisticRegression";
+string MetaTrainer = "OneVersusAll";
+string TrainerOptions = null;
+
+string OptionsInclude = "";
+string Comments= "";
+
+string ExpectedOutputPerInstance= @"// Expected output:
+            //   Label: 1, Prediction: 1
+            //   Label: 2, Prediction: 2
+            //   Label: 3, Prediction: 2
+            //   Label: 2, Prediction: 2
+            //   Label: 3, Prediction: 2";
+
+string ExpectedOutput = @"// Expected output:
+            // Expected output:
+            // Micro Accuracy: 0.90
+            // Macro Accuracy: 0.90
+            // Log Loss: 0.37
+            // Log Loss Reduction: 0.67";
+#>