diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LbfgsMaximumEntropy.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LbfgsMaximumEntropy.cs
index ab45d4ba53..7130a7c9ea 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LbfgsMaximumEntropy.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LbfgsMaximumEntropy.cs
@@ -10,39 +10,48 @@ public static class LbfgsMaximumEntropy
     {
         public static void Example()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
-            // as a catalog of available operations and as the source of randomness.
-            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
 
             // Create a list of training data points.
             var dataPoints = GenerateRandomDataPoints(1000);
 
-            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            // Convert the list of data points to an IDataView object, which is
+            // consumable by ML.NET API.
             var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
 
             // Define the trainer.
             var pipeline =
-                    // Convert the string labels into key types.
-                    mlContext.Transforms.Conversion.MapValueToKey(nameof(DataPoint.Label))
-                    // Apply LbfgsMaximumEntropy multiclass trainer.
-                    .Append(mlContext.MulticlassClassification.Trainers.LbfgsMaximumEntropy());
+                // Convert the string labels into key types.
+                mlContext.Transforms.Conversion
+                .MapValueToKey(nameof(DataPoint.Label))
+                // Apply LbfgsMaximumEntropy multiclass trainer.
+                .Append(mlContext.MulticlassClassification.Trainers
+                .LbfgsMaximumEntropy());
 
             // Train the model.
             var model = pipeline.Fit(trainingData);
 
-            // Create testing data. Use different random seed to make it different from training data.
-            var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
+            // Create testing data. Use different random seed to make it different
+            // from training data.
+            var testData = mlContext.Data
+                .LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
 
             // Run the model on test data set.
             var transformedTestData = model.Transform(testData);
 
             // Convert IDataView object to a list.
-            var predictions = mlContext.Data.CreateEnumerable<Prediction>(transformedTestData, reuseRowObject: false).ToList();
+            var predictions = mlContext.Data
+                .CreateEnumerable<Prediction>(transformedTestData,
+                reuseRowObject: false).ToList();
 
             // Look at 5 predictions
             foreach (var p in predictions.Take(5))
-                Console.WriteLine($"Label: {p.Label}, Prediction: {p.PredictedLabel}");
+                Console.WriteLine($"Label: {p.Label}, " + 
+                    $"Prediction: {p.PredictedLabel}");
 
             // Expected output:
             //   Label: 1, Prediction: 1
@@ -52,7 +61,9 @@ public static void Example()
             //   Label: 3, Prediction: 3
 
             // Evaluate the overall metrics
-            var metrics = mlContext.MulticlassClassification.Evaluate(transformedTestData);
+            var metrics = mlContext.MulticlassClassification
+                .Evaluate(transformedTestData);
+
             PrintMetrics(metrics);
             
             // Expected output:
@@ -72,8 +83,11 @@ public static void Example()
             //  Precision ||0.9308 |0.9593 |0.8580 |
         }
 
-        // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        // Generates random uniform doubles in [-0.5, 0.5)
+        // range with labels 1, 2 or 3.
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed=0)
+
         {
             var random = new Random(seed);
             float randomFloat() => (float)(random.NextDouble() - 0.5);
@@ -85,13 +99,17 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                 {
                     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()
+                    // 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.
+        // Example with label and 20 feature values. A data set is a collection of
+        // such examples.
         private class DataPoint
         {
             public uint Label { get; set; }
@@ -114,8 +132,11 @@ public static void PrintMetrics(MulticlassClassificationMetrics metrics)
             Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
             Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
             Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
-            Console.WriteLine($"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+            Console.WriteLine(
+                $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+
             Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
         }
     }
 }
+
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LbfgsMaximumEntropy.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LbfgsMaximumEntropy.tt
index a58f4f0917..519c549bd9 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LbfgsMaximumEntropy.tt
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LbfgsMaximumEntropy.tt
@@ -8,7 +8,8 @@ string TrainerOptions = null;
 string OptionsInclude = "";
 string Comments = "";
 bool CacheData = false;
-string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.";
+string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5)"
+    + "\n        // range with labels 1, 2 or 3.";
 
 string ExpectedOutputPerInstance = @"// Expected output:
             //   Label: 1, Prediction: 1
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LbfgsMaximumEntropyWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LbfgsMaximumEntropyWithOptions.cs
index a818057677..597f34af06 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LbfgsMaximumEntropyWithOptions.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LbfgsMaximumEntropyWithOptions.cs
@@ -11,15 +11,17 @@ public static class LbfgsMaximumEntropyWithOptions
     {
         public static void Example()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
-            // as a catalog of available operations and as the source of randomness.
-            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
 
             // Create a list of training data points.
             var dataPoints = GenerateRandomDataPoints(1000);
 
-            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            // Convert the list of data points to an IDataView object, which is
+            // consumable by ML.NET API.
             var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
 
             // Define trainer options.
@@ -32,27 +34,32 @@ public static void Example()
 
             // Define the trainer.
             var pipeline = 
-			        // Convert the string labels into key types.
-                    mlContext.Transforms.Conversion.MapValueToKey("Label")
-                    // Apply LbfgsMaximumEntropy multiclass trainer.
-                    .Append(mlContext.MulticlassClassification.Trainers.LbfgsMaximumEntropy(options));
-			
+                // Convert the string labels into key types.
+                mlContext.Transforms.Conversion.MapValueToKey("Label")
+                // Apply LbfgsMaximumEntropy multiclass trainer.
+                .Append(mlContext.MulticlassClassification.Trainers
+                .LbfgsMaximumEntropy(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));
+            // 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();
+            var predictions = mlContext.Data
+                .CreateEnumerable<Prediction>(transformedTestData,
+                reuseRowObject: false).ToList();
 
             // Look at 5 predictions
             foreach (var p in predictions.Take(5))
-                Console.WriteLine($"Label: {p.Label}, Prediction: {p.PredictedLabel}");
+                Console.WriteLine($"Label: {p.Label}, " + 
+                    $"Prediction: {p.PredictedLabel}");
 
             // Expected output:
             //   Label: 1, Prediction: 1
@@ -62,7 +69,9 @@ public static void Example()
             //   Label: 3, Prediction: 3
 
             // Evaluate the overall metrics
-            var metrics = mlContext.MulticlassClassification.Evaluate(transformedTestData);
+            var metrics = mlContext.MulticlassClassification
+                .Evaluate(transformedTestData);
+
             PrintMetrics(metrics);
             
             // Expected output:
@@ -82,8 +91,11 @@ public static void Example()
             //   Precision ||0.9304 |0.9593 |0.8529 |
         }
 
-        // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        // Generates random uniform doubles in [-0.5, 0.5)
+        // range with labels 1, 2 or 3.
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed=0)
+
         {
             var random = new Random(seed);
             float randomFloat() => (float)(random.NextDouble() - 0.5);
@@ -95,13 +107,17 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                 {
                     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()
+                    // 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.
+        // Example with label and 20 feature values. A data set is a collection of
+        // such examples.
         private class DataPoint
         {
             public uint Label { get; set; }
@@ -124,8 +140,11 @@ public static void PrintMetrics(MulticlassClassificationMetrics metrics)
             Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
             Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
             Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
-            Console.WriteLine($"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+            Console.WriteLine(
+                $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+
             Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
         }
     }
 }
+
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LbfgsMaximumEntropyWithOptions.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LbfgsMaximumEntropyWithOptions.tt
index 12caedbd3b..1e96c1eab9 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LbfgsMaximumEntropyWithOptions.tt
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LbfgsMaximumEntropyWithOptions.tt
@@ -12,7 +12,9 @@ string TrainerOptions = @"LbfgsMaximumEntropyMulticlassTrainer.Options
 
 string OptionsInclude = "using Microsoft.ML.Trainers;";
 string Comments = "";
-string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.";
+string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5)"
+    + "\n        // range with labels 1, 2 or 3.";
+
 bool CacheData = false;
 
 string ExpectedOutputPerInstance = @"// Expected output:
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbm.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbm.cs
index 84ac92ecef..7ab6d5f80e 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbm.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbm.cs
@@ -8,43 +8,53 @@ namespace Samples.Dynamic.Trainers.MulticlassClassification
 {
     public static class LightGbm
     {
-        // This example requires installation of additional NuGet package
-        // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
+        // This example requires installation of additional NuGet package for 
+        // Microsoft.ML.FastTree at
+        // https://www.nuget.org/packages/Microsoft.ML.FastTree/
         public static void Example()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
-            // as a catalog of available operations and as the source of randomness.
-            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
 
             // Create a list of training data points.
             var dataPoints = GenerateRandomDataPoints(1000);
 
-            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            // Convert the list of data points to an IDataView object, which is
+            // consumable by ML.NET API.
             var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
 
             // Define the trainer.
             var pipeline =
-                    // Convert the string labels into key types.
-                    mlContext.Transforms.Conversion.MapValueToKey(nameof(DataPoint.Label))
-                    // Apply LightGbm multiclass trainer.
-                    .Append(mlContext.MulticlassClassification.Trainers.LightGbm());
+                // Convert the string labels into key types.
+                mlContext.Transforms.Conversion
+                .MapValueToKey(nameof(DataPoint.Label))
+                // Apply LightGbm multiclass trainer.
+                .Append(mlContext.MulticlassClassification.Trainers
+                .LightGbm());
 
             // Train the model.
             var model = pipeline.Fit(trainingData);
 
-            // Create testing data. Use different random seed to make it different from training data.
-            var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
+            // Create testing data. Use different random seed to make it different
+            // from training data.
+            var testData = mlContext.Data
+                .LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
 
             // Run the model on test data set.
             var transformedTestData = model.Transform(testData);
 
             // Convert IDataView object to a list.
-            var predictions = mlContext.Data.CreateEnumerable<Prediction>(transformedTestData, reuseRowObject: false).ToList();
+            var predictions = mlContext.Data
+                .CreateEnumerable<Prediction>(transformedTestData,
+                reuseRowObject: false).ToList();
 
             // Look at 5 predictions
             foreach (var p in predictions.Take(5))
-                Console.WriteLine($"Label: {p.Label}, Prediction: {p.PredictedLabel}");
+                Console.WriteLine($"Label: {p.Label}, " + 
+                    $"Prediction: {p.PredictedLabel}");
 
             // Expected output:
             //   Label: 1, Prediction: 1
@@ -54,7 +64,9 @@ public static void Example()
             //   Label: 3, Prediction: 3
 
             // Evaluate the overall metrics
-            var metrics = mlContext.MulticlassClassification.Evaluate(transformedTestData);
+            var metrics = mlContext.MulticlassClassification
+                .Evaluate(transformedTestData);
+
             PrintMetrics(metrics);
             
             // Expected output:
@@ -74,8 +86,11 @@ public static void Example()
             //   Precision ||0.9936 |1.0000 |0.9701 |
         }
 
-        // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        // Generates random uniform doubles in [-0.5, 0.5)
+        // range with labels 1, 2 or 3.
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed=0)
+
         {
             var random = new Random(seed);
             float randomFloat() => (float)(random.NextDouble() - 0.5);
@@ -87,13 +102,17 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                 {
                     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()
+                    // 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.
+        // Example with label and 20 feature values. A data set is a collection of
+        // such examples.
         private class DataPoint
         {
             public uint Label { get; set; }
@@ -116,8 +135,11 @@ public static void PrintMetrics(MulticlassClassificationMetrics metrics)
             Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
             Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
             Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
-            Console.WriteLine($"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+            Console.WriteLine(
+                $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+
             Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
         }
     }
 }
+
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbm.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbm.tt
index 216ca2a0b6..47944467ba 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbm.tt
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbm.tt
@@ -7,10 +7,13 @@ string TrainerOptions = null;
 
 string OptionsInclude = "";
 string Comments = @"
-        // This example requires installation of additional NuGet package
-        // <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>.";
+        // This example requires installation of additional NuGet package for 
+        // Microsoft.ML.FastTree at
+        // https://www.nuget.org/packages/Microsoft.ML.FastTree/";
+
 bool CacheData = false;
-string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.";
+string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5)"
+    + "\n        // range with labels 1, 2 or 3.";
 
 string ExpectedOutputPerInstance = @"// Expected output:
             //   Label: 1, Prediction: 1
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbmWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbmWithOptions.cs
index 3b4444081c..76337146dc 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbmWithOptions.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbmWithOptions.cs
@@ -9,19 +9,22 @@ namespace Samples.Dynamic.Trainers.MulticlassClassification
 {
     public static class LightGbmWithOptions
     {
-        // This example requires installation of additional NuGet package
-        // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
+        // This example requires installation of additional NuGet package for 
+        // Microsoft.ML.FastTree at
+        // https://www.nuget.org/packages/Microsoft.ML.FastTree/
         public static void Example()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
-            // as a catalog of available operations and as the source of randomness.
-            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
 
             // Create a list of training data points.
             var dataPoints = GenerateRandomDataPoints(1000);
 
-            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            // Convert the list of data points to an IDataView object, which is
+            // consumable by ML.NET API.
             var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
 
             // Define trainer options.
@@ -36,27 +39,32 @@ public static void Example()
 
             // Define the trainer.
             var pipeline = 
-			        // Convert the string labels into key types.
-                    mlContext.Transforms.Conversion.MapValueToKey("Label")
-                    // Apply LightGbm multiclass trainer.
-                    .Append(mlContext.MulticlassClassification.Trainers.LightGbm(options));
-			
+                // Convert the string labels into key types.
+                mlContext.Transforms.Conversion.MapValueToKey("Label")
+                // Apply LightGbm multiclass trainer.
+                .Append(mlContext.MulticlassClassification.Trainers
+                .LightGbm(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));
+            // 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();
+            var predictions = mlContext.Data
+                .CreateEnumerable<Prediction>(transformedTestData,
+                reuseRowObject: false).ToList();
 
             // Look at 5 predictions
             foreach (var p in predictions.Take(5))
-                Console.WriteLine($"Label: {p.Label}, Prediction: {p.PredictedLabel}");
+                Console.WriteLine($"Label: {p.Label}, " + 
+                    $"Prediction: {p.PredictedLabel}");
 
             // Expected output:
             //   Label: 1, Prediction: 1
@@ -66,7 +74,9 @@ public static void Example()
             //   Label: 3, Prediction: 3
 
             // Evaluate the overall metrics
-            var metrics = mlContext.MulticlassClassification.Evaluate(transformedTestData);
+            var metrics = mlContext.MulticlassClassification
+                .Evaluate(transformedTestData);
+
             PrintMetrics(metrics);
             
             // Expected output:
@@ -86,8 +96,11 @@ public static void Example()
             //   Precision ||0.9936 |1.0000 |0.9419 |
         }
 
-        // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        // Generates random uniform doubles in [-0.5, 0.5)
+        // range with labels 1, 2 or 3.
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed=0)
+
         {
             var random = new Random(seed);
             float randomFloat() => (float)(random.NextDouble() - 0.5);
@@ -99,13 +112,17 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                 {
                     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()
+                    // 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.
+        // Example with label and 20 feature values. A data set is a collection of
+        // such examples.
         private class DataPoint
         {
             public uint Label { get; set; }
@@ -128,8 +145,11 @@ public static void PrintMetrics(MulticlassClassificationMetrics metrics)
             Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
             Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
             Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
-            Console.WriteLine($"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+            Console.WriteLine(
+                $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+
             Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
         }
     }
 }
+
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbmWithOptions.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbmWithOptions.tt
index 7ec6706227..b267db33f0 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbmWithOptions.tt
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbmWithOptions.tt
@@ -14,11 +14,13 @@ string TrainerOptions = @"LightGbmMulticlassTrainer.Options
 
 string OptionsInclude = "using Microsoft.ML.Trainers.LightGbm;";
 string Comments = @"
-        // This example requires installation of additional NuGet package
-        // <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>.";
+        // This example requires installation of additional NuGet package for 
+        // Microsoft.ML.FastTree at
+        // https://www.nuget.org/packages/Microsoft.ML.FastTree/";
 
 bool CacheData = false;
-string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.";
+string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5)"
+    + "\n        // range with labels 1, 2 or 3.";
 
 string ExpectedOutputPerInstance = @"// Expected output:
             //   Label: 1, Prediction: 1
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LogLossPerClass.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LogLossPerClass.cs
index 86ab646083..8b1076f830 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LogLossPerClass.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LogLossPerClass.cs
@@ -10,42 +10,50 @@ public static class LogLossPerClass
     {
         public static void Example()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
-            // as a catalog of available operations and as the source of randomness.
-            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
 
             // Create a list of training data points.
             var dataPoints = GenerateRandomDataPoints(1000);
 
-            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            // Convert the list of data points to an IDataView object, which is
+            // consumable by ML.NET API.
             var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
 
             // Define the trainer.
             var pipeline =
-                    // Convert the string labels into key types.
-                    mlContext.Transforms.Conversion.MapValueToKey(nameof(DataPoint.Label))
-                    // Apply a multiclass trainer.
-                    .Append(mlContext.MulticlassClassification.Trainers.LightGbm());
+                // Convert the string labels into key types.
+                mlContext.Transforms.Conversion
+                .MapValueToKey(nameof(DataPoint.Label))
+                // Apply a multiclass trainer.
+                .Append(mlContext.MulticlassClassification.Trainers
+                .LightGbm());
 
             // Train the model.
             var model = pipeline.Fit(trainingData);
 
-            // Create testing data. Use different random seed to make it different from training data.
-            var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
+            // Create testing data. Use different random seed to make it different
+            // from training data.
+            var testData = mlContext.Data
+                .LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
 
             // Run the model on test data set.
             var transformedTestData = model.Transform(testData);
 
             // Evaluate the overall metrics
-            var metrics = mlContext.MulticlassClassification.Evaluate(transformedTestData);
+            var metrics = mlContext.MulticlassClassification
+                .Evaluate(transformedTestData);
 
             // Find the original label values.
             VBuffer<uint> keys = default;
             transformedTestData.Schema["PredictedLabel"].GetKeyValues(ref keys);
             var originalLabels = keys.DenseValues().ToArray();
             for (var i = 0; i < originalLabels.Length; i++)
-                Console.WriteLine($"LogLoss for label {originalLabels[i]}: {metrics.PerClassLogLoss[i]:F4}");
+                Console.WriteLine($"LogLoss for label " +
+                    $"{originalLabels[i]}: {metrics.PerClassLogLoss[i]:F4}");
 
             // Expected output:
             //   LogLoss for label 7: 0.2578
@@ -60,7 +68,9 @@ public static void Example()
         }
 
         // Generates data points with random features and labels 1 to 9.
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0)
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed = 0)
+
         {
             var random = new Random(seed);
             float randomFloat() => (float)(random.NextDouble() - 0.5);
@@ -72,13 +82,17 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                 {
                     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()
+                    // 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.
+        // Example with label and 20 feature values. A data set is a collection of
+        // such examples.
         private class DataPoint
         {
             public uint Label { get; set; }
@@ -95,4 +109,4 @@ private class Prediction
             public uint PredictedLabel { get; set; }
         }
     }
-}
+}
\ No newline at end of file
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/MulticlassClassification.ttinclude b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/MulticlassClassification.ttinclude
index 87201f12a3..ee3f245566 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/MulticlassClassification.ttinclude
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/MulticlassClassification.ttinclude
@@ -13,79 +13,97 @@ namespace Samples.Dynamic.Trainers.MulticlassClassification
     {<#=Comments#>
         public static void Example()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
-            // as a catalog of available operations and as the source of randomness.
-            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
 
             // Create a list of training data points.
             var dataPoints = GenerateRandomDataPoints(1000);
 
-            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            // Convert the list of data points to an IDataView object, which is
+            // consumable by ML.NET API.
             var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
 <# if (CacheData) { #>
 
-            // ML.NET doesn't cache data set by default. Therefore, if one reads a data set from a file and accesses it many times,
-			// it can be slow due to expensive featurization and disk operations. When the considered data can fit into memory,
-			// a solution is to cache the data in memory. Caching is especially helpful when working with iterative algorithms 
-			// which needs many data passes.
-			trainingData = mlContext.Data.Cache(trainingData);
+            // ML.NET doesn't cache data set by default. Therefore, if one reads a
+            // data set from a file and accesses it many times, it can be slow due
+            // to expensive featurization and disk operations. When the considered
+            // data can fit into memory, a solution is to cache the data in memory.
+            // Caching is especially helpful when working with iterative algorithms 
+            // which needs many data passes.
+            trainingData = mlContext.Data.Cache(trainingData);
 <# } #>
 
 <# 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#>()));
+                // 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(nameof(DataPoint.Label))
-                    // Apply <#=Trainer#> multiclass trainer.
-                    .Append(mlContext.MulticlassClassification.Trainers.<#=Trainer#>());
+                // Convert the string labels into key types.
+                mlContext.Transforms.Conversion
+                .MapValueToKey(nameof(DataPoint.Label))
+                // Apply <#=Trainer#> multiclass trainer.
+                .Append(mlContext.MulticlassClassification.Trainers
+                .<#=Trainer#>());
 <# } else { #>
             // Define trainer options.
             var options = new <#=TrainerOptions#>;
 
             // 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#>(options));
-			
+                // Convert the string labels into key types.
+                mlContext.Transforms.Conversion.MapValueToKey("Label")
+                // Apply <#=Trainer#> multiclass trainer.
+                .Append(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));
+            // 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();
+            var predictions = mlContext.Data
+                .CreateEnumerable<Prediction>(transformedTestData,
+                reuseRowObject: false).ToList();
 
             // Look at 5 predictions
             foreach (var p in predictions.Take(5))
-                Console.WriteLine($"Label: {p.Label}, Prediction: {p.PredictedLabel}");
+                Console.WriteLine($"Label: {p.Label}, " + 
+                    $"Prediction: {p.PredictedLabel}");
 
             <#=ExpectedOutputPerInstance#>
 
             // Evaluate the overall metrics
-            var metrics = mlContext.MulticlassClassification.Evaluate(transformedTestData);
+            var metrics = mlContext.MulticlassClassification
+                .Evaluate(transformedTestData);
+
             PrintMetrics(metrics);
             
             <#=ExpectedOutput#>
         }
 
         <#=DataGenerationComments#>
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed=0)
+
         {
             var random = new Random(seed);
             float randomFloat() => (float)(random.NextDouble() - 0.5);
@@ -97,13 +115,17 @@ namespace Samples.Dynamic.Trainers.MulticlassClassification
                 {
                     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()
+                    // 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.
+        // Example with label and 20 feature values. A data set is a collection of
+        // such examples.
         private class DataPoint
         {
             public uint Label { get; set; }
@@ -126,8 +148,10 @@ namespace Samples.Dynamic.Trainers.MulticlassClassification
             Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
             Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
             Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
-            Console.WriteLine($"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+            Console.WriteLine(
+                $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+
             Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
         }
     }
-}
\ No newline at end of file
+}
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/NaiveBayes.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/NaiveBayes.cs
index d76006bf2c..4dc7f44f9e 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/NaiveBayes.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/NaiveBayes.cs
@@ -9,44 +9,55 @@ namespace Samples.Dynamic.Trainers.MulticlassClassification
     public static class NaiveBayes
     {
         // Naive Bayes classifier is based on Bayes' theorem. 
-        // It assumes independence among the presence of features in a class even though they may be dependent on each other.
-        // It is  a multi-class trainer that accepts binary feature values of type float, i.e., feature values are either true or false.
-        // Specifically a feature value greater than zero is treated as true, zero or less is treated as false.
+        // It assumes independence among the presence of features in a class even
+        // though they may be dependent on each other. It is  a multi-class trainer
+        // that accepts binary feature values of type float, i.e., feature values
+        // are either true or false. Specifically a feature value greater than zero
+        // is treated as true, zero or less is treated as false.
         public static void Example()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
-            // as a catalog of available operations and as the source of randomness.
-            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
 
             // Create a list of training data points.
             var dataPoints = GenerateRandomDataPoints(1000);
 
-            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            // Convert the list of data points to an IDataView object, which is
+            // consumable by ML.NET API.
             var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
 
             // Define the trainer.
             var pipeline =
-                    // Convert the string labels into key types.
-                    mlContext.Transforms.Conversion.MapValueToKey(nameof(DataPoint.Label))
-                    // Apply NaiveBayes multiclass trainer.
-                    .Append(mlContext.MulticlassClassification.Trainers.NaiveBayes());
+                // Convert the string labels into key types.
+                mlContext.Transforms.Conversion
+                .MapValueToKey(nameof(DataPoint.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));
+            // 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();
+            var predictions = mlContext.Data
+                .CreateEnumerable<Prediction>(transformedTestData,
+                reuseRowObject: false).ToList();
 
             // Look at 5 predictions
             foreach (var p in predictions.Take(5))
-                Console.WriteLine($"Label: {p.Label}, Prediction: {p.PredictedLabel}");
+                Console.WriteLine($"Label: {p.Label}, " + 
+                    $"Prediction: {p.PredictedLabel}");
 
             // Expected output:
             //   Label: 1, Prediction: 1
@@ -56,7 +67,9 @@ public static void Example()
             //   Label: 3, Prediction: 3
 
             // Evaluate the overall metrics
-            var metrics = mlContext.MulticlassClassification.Evaluate(transformedTestData);
+            var metrics = mlContext.MulticlassClassification
+                .Evaluate(transformedTestData);
+
             PrintMetrics(metrics);
             
             // Expected output:
@@ -77,9 +90,12 @@ public static void Example()
         }
 
         
-        // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.
-        // For NaiveBayes values greater than zero are treated as true, zero or less are treated as false.
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        // Generates random uniform doubles in [-0.5, 0.5) range with labels
+        // 1, 2 or 3. For NaiveBayes values greater than zero are treated as true,
+        // zero or less are treated as false.
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed=0)
+
         {
             var random = new Random(seed);
             float randomFloat() => (float)(random.NextDouble() - 0.5);
@@ -91,13 +107,17 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                 {
                     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()
+                    // 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.
+        // Example with label and 20 feature values. A data set is a collection of
+        // such examples.
         private class DataPoint
         {
             public uint Label { get; set; }
@@ -120,8 +140,11 @@ public static void PrintMetrics(MulticlassClassificationMetrics metrics)
             Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
             Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
             Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
-            Console.WriteLine($"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+            Console.WriteLine(
+                $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+
             Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
         }
     }
 }
+
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/NaiveBayes.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/NaiveBayes.tt
index a74ad64a80..26e0162b12 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/NaiveBayes.tt
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/NaiveBayes.tt
@@ -9,13 +9,16 @@ bool CacheData = false;
 string OptionsInclude = "";
 string Comments= @"
         // Naive Bayes classifier is based on Bayes' theorem. 
-        // It assumes independence among the presence of features in a class even though they may be dependent on each other.
-        // It is  a multi-class trainer that accepts binary feature values of type float, i.e., feature values are either true or false.
-        // Specifically a feature value greater than zero is treated as true, zero or less is treated as false.";
+        // It assumes independence among the presence of features in a class even
+        // though they may be dependent on each other. It is  a multi-class trainer
+        // that accepts binary feature values of type float, i.e., feature values
+        // are either true or false. Specifically a feature value greater than zero
+        // is treated as true, zero or less is treated as false.";
 
 string DataGenerationComments= @"
-        // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.
-        // For NaiveBayes values greater than zero are treated as true, zero or less are treated as false.";
+        // Generates random uniform doubles in [-0.5, 0.5) range with labels
+        // 1, 2 or 3. For NaiveBayes values greater than zero are treated as true,
+        // zero or less are treated as false.";
 
 string ExpectedOutputPerInstance= @"// Expected output:
             //   Label: 1, Prediction: 1
@@ -39,4 +42,4 @@ string ExpectedOutput = @"// Expected output:
             //           2 ||     9 |    21 |   133 | 0.8160
             //             ||========================
             //   Precision ||0.9467 |0.8735 |0.8061 |";
-#>
\ No newline at end of file
+#>
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/OneVersusAll.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/OneVersusAll.cs
index 9b93fd3cd0..40a040325c 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/OneVersusAll.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/OneVersusAll.cs
@@ -10,39 +10,49 @@ 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.
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
 
             // Create a list of training data points.
             var dataPoints = GenerateRandomDataPoints(1000);
 
-            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            // Convert the list of data points to an IDataView object, which is
+            // consumable by ML.NET API.
             var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
 
             // Define the trainer.
             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()));
+                // 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));
+            // 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();
+            var predictions = mlContext.Data
+                .CreateEnumerable<Prediction>(transformedTestData,
+                reuseRowObject: false).ToList();
 
             // Look at 5 predictions
             foreach (var p in predictions.Take(5))
-                Console.WriteLine($"Label: {p.Label}, Prediction: {p.PredictedLabel}");
+                Console.WriteLine($"Label: {p.Label}, " + 
+                    $"Prediction: {p.PredictedLabel}");
 
             // Expected output:
             //   Label: 1, Prediction: 1
@@ -52,7 +62,9 @@ public static void Example()
             //   Label: 3, Prediction: 2
 
             // Evaluate the overall metrics
-            var metrics = mlContext.MulticlassClassification.Evaluate(transformedTestData);
+            var metrics = mlContext.MulticlassClassification
+                .Evaluate(transformedTestData);
+
             PrintMetrics(metrics);
             
             // Expected output:
@@ -72,8 +84,11 @@ public static void Example()
             //   Precision ||0.8994 |0.9180 |0.8851 |
         }
 
-        // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        // Generates random uniform doubles in [-0.5, 0.5)
+        // range with labels 1, 2 or 3.
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed=0)
+
         {
             var random = new Random(seed);
             float randomFloat() => (float)(random.NextDouble() - 0.5);
@@ -85,13 +100,17 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                 {
                     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()
+                    // 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.
+        // Example with label and 20 feature values. A data set is a collection of
+        // such examples.
         private class DataPoint
         {
             public uint Label { get; set; }
@@ -114,8 +133,11 @@ public static void PrintMetrics(MulticlassClassificationMetrics metrics)
             Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
             Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
             Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
-            Console.WriteLine($"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+            Console.WriteLine(
+                $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+
             Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
         }
     }
 }
+
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/OneVersusAll.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/OneVersusAll.tt
index 0233d9948e..ce355c56b0 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/OneVersusAll.tt
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/OneVersusAll.tt
@@ -7,7 +7,8 @@ string TrainerOptions = null;
 
 string OptionsInclude = "";
 string Comments= "";
-string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.";
+string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5)"
+    + "\n        // range with labels 1, 2 or 3.";
 
 bool CacheData = false;
 
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/PairwiseCoupling.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/PairwiseCoupling.cs
index de0dab8a9c..be42ec3bbd 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/PairwiseCoupling.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/PairwiseCoupling.cs
@@ -10,39 +10,49 @@ public static class PairwiseCoupling
     {
         public static void Example()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
-            // as a catalog of available operations and as the source of randomness.
-            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
 
             // Create a list of training data points.
             var dataPoints = GenerateRandomDataPoints(1000);
 
-            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            // Convert the list of data points to an IDataView object, which is
+            // consumable by ML.NET API.
             var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
 
             // Define the trainer.
             var pipeline =
-                    // Convert the string labels into key types.
-                    mlContext.Transforms.Conversion.MapValueToKey("Label")
-                    // Apply PairwiseCoupling multiclass meta trainer on top of binary trainer.
-                    .Append(mlContext.MulticlassClassification.Trainers.PairwiseCoupling(mlContext.BinaryClassification.Trainers.SdcaLogisticRegression()));
+                // Convert the string labels into key types.
+                mlContext.Transforms.Conversion.MapValueToKey("Label")
+                // Apply PairwiseCoupling multiclass meta trainer on top of
+                // binary trainer.
+                .Append(mlContext.MulticlassClassification.Trainers
+                .PairwiseCoupling(
+                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));
+            // 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();
+            var predictions = mlContext.Data
+                .CreateEnumerable<Prediction>(transformedTestData,
+                reuseRowObject: false).ToList();
 
             // Look at 5 predictions
             foreach (var p in predictions.Take(5))
-                Console.WriteLine($"Label: {p.Label}, Prediction: {p.PredictedLabel}");
+                Console.WriteLine($"Label: {p.Label}, " + 
+                    $"Prediction: {p.PredictedLabel}");
 
             // Expected output:
             //   Label: 1, Prediction: 1
@@ -52,7 +62,9 @@ public static void Example()
             //   Label: 3, Prediction: 2
 
             // Evaluate the overall metrics
-            var metrics = mlContext.MulticlassClassification.Evaluate(transformedTestData);
+            var metrics = mlContext.MulticlassClassification
+                .Evaluate(transformedTestData);
+
             PrintMetrics(metrics);
             
             // Expected output:
@@ -72,8 +84,11 @@ public static void Example()
             //   Precision ||0.9091 |0.9171 |0.8636 |
         }
 
-        // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        // Generates random uniform doubles in [-0.5, 0.5)
+        // range with labels 1, 2 or 3.
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed=0)
+
         {
             var random = new Random(seed);
             float randomFloat() => (float)(random.NextDouble() - 0.5);
@@ -85,13 +100,17 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                 {
                     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()
+                    // 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.
+        // Example with label and 20 feature values. A data set is a collection of
+        // such examples.
         private class DataPoint
         {
             public uint Label { get; set; }
@@ -114,8 +133,11 @@ public static void PrintMetrics(MulticlassClassificationMetrics metrics)
             Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
             Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
             Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
-            Console.WriteLine($"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+            Console.WriteLine(
+                $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+
             Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
         }
     }
 }
+
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/PairwiseCoupling.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/PairwiseCoupling.tt
index 4f3617e693..a568fd9736 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/PairwiseCoupling.tt
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/PairwiseCoupling.tt
@@ -7,7 +7,9 @@ string TrainerOptions = null;
 
 string OptionsInclude = "";
 string Comments= "";
-string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.";
+string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5)"
+    + "\n        // range with labels 1, 2 or 3.";
+
 bool CacheData = false;
 
 string ExpectedOutputPerInstance= @"// Expected output:
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/PermutationFeatureImportance.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/PermutationFeatureImportance.cs
index 963fd238ca..71d1c02106 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/PermutationFeatureImportance.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/PermutationFeatureImportance.cs
@@ -9,9 +9,10 @@ public static class PermutationFeatureImportance
     {
         public static void Example()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
-            // as a catalog of available operations and as the source of randomness.
-            var mlContext = new MLContext(seed:1);
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness.
+            var mlContext = new MLContext(seed: 1);
 
             // Create sample data.
             var samples = GenerateData();
@@ -19,13 +20,17 @@ public static void Example()
             // Load the sample data as an IDataView.
             var data = mlContext.Data.LoadFromEnumerable(samples);
 
-            // Define a training pipeline that concatenates features into a vector, normalizes them, and then
-            // trains a linear model.
-            var featureColumns = new string[] { nameof(Data.Feature1), nameof(Data.Feature2) };
-            var pipeline = mlContext.Transforms.Concatenate("Features", featureColumns)
-                    .Append(mlContext.Transforms.Conversion.MapValueToKey("Label"))
-                    .Append(mlContext.Transforms.NormalizeMinMax("Features"))
-                    .Append(mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy());
+            // Define a training pipeline that concatenates features into a vector,
+            // normalizes them, and then trains a linear model.
+            var featureColumns =
+                new string[] { nameof(Data.Feature1), nameof(Data.Feature2) };
+
+            var pipeline = mlContext.Transforms
+                .Concatenate("Features", featureColumns)
+                .Append(mlContext.Transforms.Conversion.MapValueToKey("Label"))
+                .Append(mlContext.Transforms.NormalizeMinMax("Features"))
+                .Append(mlContext.MulticlassClassification.Trainers
+                .SdcaMaximumEntropy());
 
             // Fit the pipeline to the data.
             var model = pipeline.Fit(data);
@@ -36,18 +41,26 @@ public static void Example()
             // Extract the predictor.
             var linearPredictor = model.LastTransformer;
 
-            // Compute the permutation metrics for the linear model using the normalized data.
-            var permutationMetrics = mlContext.MulticlassClassification.PermutationFeatureImportance(
-                linearPredictor, transformedData, permutationCount: 30);
-
-            // Now let's look at which features are most important to the model overall.
-            // Get the feature indices sorted by their impact on microaccuracy.
-            var sortedIndices = permutationMetrics.Select((metrics, index) => new { index, metrics.MicroAccuracy})
+            // Compute the permutation metrics for the linear model using the
+            // normalized data.
+            var permutationMetrics = mlContext.MulticlassClassification
+                .PermutationFeatureImportance(linearPredictor, transformedData,
+                permutationCount: 30);
+
+            // Now let's look at which features are most important to the model
+            // overall. Get the feature indices sorted by their impact on
+            // microaccuracy.
+            var sortedIndices = permutationMetrics
+                .Select((metrics, index) => new { index, metrics.MicroAccuracy })
                 .OrderByDescending(feature => Math.Abs(feature.MicroAccuracy.Mean))
                 .Select(feature => feature.index);
 
-            Console.WriteLine("Feature\tChange in MicroAccuracy\t95% Confidence in the Mean Change in MicroAccuracy");
-            var microAccuracy = permutationMetrics.Select(x => x.MicroAccuracy).ToArray();
+            Console.WriteLine("Feature\tChange in MicroAccuracy\t95% Confidence in "
+                + "the Mean Change in MicroAccuracy");
+
+            var microAccuracy = permutationMetrics.Select(x => x.MicroAccuracy)
+                .ToArray();
+
             foreach (int i in sortedIndices)
             {
                 Console.WriteLine("{0}\t{1:G4}\t{2:G4}",
@@ -76,10 +89,14 @@ private class Data
         /// linear combination of the features.
         /// </summary>
         /// <param name="nExamples">The number of examples.</param>
-        /// <param name="bias">The bias, or offset, in the calculation of the label.</param>
-        /// <param name="weight1">The weight to multiply the first feature with to compute the label.</param>
-        /// <param name="weight2">The weight to multiply the second feature with to compute the label.</param>
-        /// <param name="seed">The seed for generating feature values and label noise.</param>
+        /// <param name="bias">The bias, or offset, in the calculation of the
+        /// label.</param>
+        /// <param name="weight1">The weight to multiply the first feature with to
+        /// compute the label.</param>
+        /// <param name="weight2">The weight to multiply the second feature with to
+        /// compute the label.</param>
+        /// <param name="seed">The seed for generating feature values and label
+        /// noise.</param>
         /// <returns>An enumerable of Data objects.</returns>
         private static IEnumerable<Data> GenerateData(int nExamples = 10000,
             double bias = 0, double weight1 = 1, double weight2 = 2, int seed = 1)
@@ -95,7 +112,10 @@ private static IEnumerable<Data> GenerateData(int nExamples = 10000,
                 };
 
                 // Create a noisy label.
-                var value = (float)(bias + weight1 * data.Feature1 + weight2 * data.Feature2 + rng.NextDouble() - 0.5);
+                var value = (float)
+                    (bias + weight1 * data.Feature1 + weight2 * data.Feature2 +
+                    rng.NextDouble() - 0.5);
+
                 if (value < max / 3)
                     data.Label = 0;
                 else if (value < 2 * max / 3)
@@ -106,4 +126,4 @@ private static IEnumerable<Data> GenerateData(int nExamples = 10000,
             }
         }
     }
-}
+}
\ No newline at end of file
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaMaximumEntropy.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaMaximumEntropy.cs
index 1cc66a33cc..2a83a84f3a 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaMaximumEntropy.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaMaximumEntropy.cs
@@ -10,45 +10,56 @@ public static class SdcaMaximumEntropy
     {
         public static void Example()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
-            // as a catalog of available operations and as the source of randomness.
-            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
 
             // Create a list of training data points.
             var dataPoints = GenerateRandomDataPoints(1000);
 
-            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            // Convert the list of data points to an IDataView object, which is
+            // consumable by ML.NET API.
             var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
 
-            // ML.NET doesn't cache data set by default. Therefore, if one reads a data set from a file and accesses it many times,
-			// it can be slow due to expensive featurization and disk operations. When the considered data can fit into memory,
-			// a solution is to cache the data in memory. Caching is especially helpful when working with iterative algorithms 
-			// which needs many data passes.
-			trainingData = mlContext.Data.Cache(trainingData);
+            // ML.NET doesn't cache data set by default. Therefore, if one reads a
+            // data set from a file and accesses it many times, it can be slow due
+            // to expensive featurization and disk operations. When the considered
+            // data can fit into memory, a solution is to cache the data in memory.
+            // Caching is especially helpful when working with iterative algorithms 
+            // which needs many data passes.
+            trainingData = mlContext.Data.Cache(trainingData);
 
             // Define the trainer.
             var pipeline =
-                    // Convert the string labels into key types.
-                    mlContext.Transforms.Conversion.MapValueToKey(nameof(DataPoint.Label))
-                    // Apply SdcaMaximumEntropy multiclass trainer.
-                    .Append(mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy());
+                // Convert the string labels into key types.
+                mlContext.Transforms.Conversion
+                .MapValueToKey(nameof(DataPoint.Label))
+                // Apply SdcaMaximumEntropy multiclass trainer.
+                .Append(mlContext.MulticlassClassification.Trainers
+                .SdcaMaximumEntropy());
 
             // Train the model.
             var model = pipeline.Fit(trainingData);
 
-            // Create testing data. Use different random seed to make it different from training data.
-            var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
+            // Create testing data. Use different random seed to make it different
+            // from training data.
+            var testData = mlContext.Data
+                .LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
 
             // Run the model on test data set.
             var transformedTestData = model.Transform(testData);
 
             // Convert IDataView object to a list.
-            var predictions = mlContext.Data.CreateEnumerable<Prediction>(transformedTestData, reuseRowObject: false).ToList();
+            var predictions = mlContext.Data
+                .CreateEnumerable<Prediction>(transformedTestData,
+                reuseRowObject: false).ToList();
 
             // Look at 5 predictions
             foreach (var p in predictions.Take(5))
-                Console.WriteLine($"Label: {p.Label}, Prediction: {p.PredictedLabel}");
+                Console.WriteLine($"Label: {p.Label}, " + 
+                    $"Prediction: {p.PredictedLabel}");
 
             // Expected output:
             //   Label: 1, Prediction: 1
@@ -58,7 +69,9 @@ public static void Example()
             //   Label: 3, Prediction: 3
 
             // Evaluate the overall metrics
-            var metrics = mlContext.MulticlassClassification.Evaluate(transformedTestData);
+            var metrics = mlContext.MulticlassClassification
+                .Evaluate(transformedTestData);
+
             PrintMetrics(metrics);
             
             // Expected output:
@@ -77,8 +90,11 @@ public static void Example()
             //   Precision ||0.9130 |0.9538 |0.8494 |
         }
 
-        // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        // Generates random uniform doubles in [-0.5, 0.5)
+        // range with labels 1, 2 or 3.
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed=0)
+
         {
             var random = new Random(seed);
             float randomFloat() => (float)(random.NextDouble() - 0.5);
@@ -90,13 +106,17 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                 {
                     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()
+                    // 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.
+        // Example with label and 20 feature values. A data set is a collection of
+        // such examples.
         private class DataPoint
         {
             public uint Label { get; set; }
@@ -119,8 +139,11 @@ public static void PrintMetrics(MulticlassClassificationMetrics metrics)
             Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
             Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
             Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
-            Console.WriteLine($"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+            Console.WriteLine(
+                $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+
             Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
         }
     }
 }
+
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaMaximumEntropy.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaMaximumEntropy.tt
index 4eccf95005..f83d618268 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaMaximumEntropy.tt
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaMaximumEntropy.tt
@@ -8,7 +8,8 @@ string TrainerOptions = null;
 string OptionsInclude = "";
 string Comments = "";
 bool CacheData = true;
-string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.";
+string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5)" 
+    + "\n        // range with labels 1, 2 or 3.";
 
 string ExpectedOutputPerInstance = @"// Expected output:
             //   Label: 1, Prediction: 1
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaMaximumEntropyWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaMaximumEntropyWithOptions.cs
index a3cfcf4cbc..62ed191c0f 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaMaximumEntropyWithOptions.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaMaximumEntropyWithOptions.cs
@@ -11,22 +11,26 @@ public static class SdcaMaximumEntropyWithOptions
     {
         public static void Example()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
-            // as a catalog of available operations and as the source of randomness.
-            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
 
             // Create a list of training data points.
             var dataPoints = GenerateRandomDataPoints(1000);
 
-            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            // Convert the list of data points to an IDataView object, which is
+            // consumable by ML.NET API.
             var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
 
-            // ML.NET doesn't cache data set by default. Therefore, if one reads a data set from a file and accesses it many times,
-			// it can be slow due to expensive featurization and disk operations. When the considered data can fit into memory,
-			// a solution is to cache the data in memory. Caching is especially helpful when working with iterative algorithms 
-			// which needs many data passes.
-			trainingData = mlContext.Data.Cache(trainingData);
+            // ML.NET doesn't cache data set by default. Therefore, if one reads a
+            // data set from a file and accesses it many times, it can be slow due
+            // to expensive featurization and disk operations. When the considered
+            // data can fit into memory, a solution is to cache the data in memory.
+            // Caching is especially helpful when working with iterative algorithms 
+            // which needs many data passes.
+            trainingData = mlContext.Data.Cache(trainingData);
 
             // Define trainer options.
             var options = new SdcaMaximumEntropyMulticlassTrainer.Options
@@ -39,27 +43,32 @@ public static void Example()
 
             // Define the trainer.
             var pipeline = 
-			        // Convert the string labels into key types.
-                    mlContext.Transforms.Conversion.MapValueToKey("Label")
-                    // Apply SdcaMaximumEntropy multiclass trainer.
-                    .Append(mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy(options));
-			
+                // Convert the string labels into key types.
+                mlContext.Transforms.Conversion.MapValueToKey("Label")
+                // Apply SdcaMaximumEntropy multiclass trainer.
+                .Append(mlContext.MulticlassClassification.Trainers
+                .SdcaMaximumEntropy(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));
+            // 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();
+            var predictions = mlContext.Data
+                .CreateEnumerable<Prediction>(transformedTestData,
+                reuseRowObject: false).ToList();
 
             // Look at 5 predictions
             foreach (var p in predictions.Take(5))
-                Console.WriteLine($"Label: {p.Label}, Prediction: {p.PredictedLabel}");
+                Console.WriteLine($"Label: {p.Label}, " + 
+                    $"Prediction: {p.PredictedLabel}");
 
             // Expected output:
             //   Label: 1, Prediction: 1
@@ -69,7 +78,9 @@ public static void Example()
             //   Label: 3, Prediction: 3
 
             // Evaluate the overall metrics
-            var metrics = mlContext.MulticlassClassification.Evaluate(transformedTestData);
+            var metrics = mlContext.MulticlassClassification
+                .Evaluate(transformedTestData);
+
             PrintMetrics(metrics);
             
             // Expected output:
@@ -89,8 +100,11 @@ public static void Example()
             //   Precision ||0.9363 |0.9647 |0.8497 |
         }
 
-        // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        // Generates random uniform doubles in [-0.5, 0.5)
+        // range with labels 1, 2 or 3.
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed=0)
+
         {
             var random = new Random(seed);
             float randomFloat() => (float)(random.NextDouble() - 0.5);
@@ -102,13 +116,17 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                 {
                     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()
+                    // 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.
+        // Example with label and 20 feature values. A data set is a collection of
+        // such examples.
         private class DataPoint
         {
             public uint Label { get; set; }
@@ -131,8 +149,11 @@ public static void PrintMetrics(MulticlassClassificationMetrics metrics)
             Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
             Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
             Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
-            Console.WriteLine($"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+            Console.WriteLine(
+                $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+
             Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
         }
     }
 }
+
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaMaximumEntropyWithOptions.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaMaximumEntropyWithOptions.tt
index 88eead7e0d..f6bb41ac40 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaMaximumEntropyWithOptions.tt
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaMaximumEntropyWithOptions.tt
@@ -14,7 +14,8 @@ string TrainerOptions = @"SdcaMaximumEntropyMulticlassTrainer.Options
 string OptionsInclude = "using Microsoft.ML.Trainers;";
 string Comments = "";
 bool CacheData = true;
-string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.";
+string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5)" 
+    + "\n        // range with labels 1, 2 or 3.";
 
 
 string ExpectedOutputPerInstance = @"// Expected output:
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaNonCalibrated.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaNonCalibrated.cs
index d9f59fe3e7..6a899f9432 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaNonCalibrated.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaNonCalibrated.cs
@@ -10,45 +10,56 @@ public static class SdcaNonCalibrated
     {
         public static void Example()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
-            // as a catalog of available operations and as the source of randomness.
-            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
 
             // Create a list of training data points.
             var dataPoints = GenerateRandomDataPoints(1000);
 
-            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            // Convert the list of data points to an IDataView object, which is
+            // consumable by ML.NET API.
             var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
 
-            // ML.NET doesn't cache data set by default. Therefore, if one reads a data set from a file and accesses it many times,
-			// it can be slow due to expensive featurization and disk operations. When the considered data can fit into memory,
-			// a solution is to cache the data in memory. Caching is especially helpful when working with iterative algorithms 
-			// which needs many data passes.
-			trainingData = mlContext.Data.Cache(trainingData);
+            // ML.NET doesn't cache data set by default. Therefore, if one reads a
+            // data set from a file and accesses it many times, it can be slow due
+            // to expensive featurization and disk operations. When the considered
+            // data can fit into memory, a solution is to cache the data in memory.
+            // Caching is especially helpful when working with iterative algorithms 
+            // which needs many data passes.
+            trainingData = mlContext.Data.Cache(trainingData);
 
             // Define the trainer.
             var pipeline =
-                    // Convert the string labels into key types.
-                    mlContext.Transforms.Conversion.MapValueToKey(nameof(DataPoint.Label))
-                    // Apply SdcaNonCalibrated multiclass trainer.
-                    .Append(mlContext.MulticlassClassification.Trainers.SdcaNonCalibrated());
+                // Convert the string labels into key types.
+                mlContext.Transforms.Conversion
+                .MapValueToKey(nameof(DataPoint.Label))
+                // Apply SdcaNonCalibrated multiclass trainer.
+                .Append(mlContext.MulticlassClassification.Trainers
+                .SdcaNonCalibrated());
 
             // Train the model.
             var model = pipeline.Fit(trainingData);
 
-            // Create testing data. Use different random seed to make it different from training data.
-            var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
+            // Create testing data. Use different random seed to make it different
+            // from training data.
+            var testData = mlContext.Data
+                .LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
 
             // Run the model on test data set.
             var transformedTestData = model.Transform(testData);
 
             // Convert IDataView object to a list.
-            var predictions = mlContext.Data.CreateEnumerable<Prediction>(transformedTestData, reuseRowObject: false).ToList();
+            var predictions = mlContext.Data
+                .CreateEnumerable<Prediction>(transformedTestData,
+                reuseRowObject: false).ToList();
 
             // Look at 5 predictions
             foreach (var p in predictions.Take(5))
-                Console.WriteLine($"Label: {p.Label}, Prediction: {p.PredictedLabel}");
+                Console.WriteLine($"Label: {p.Label}, " + 
+                    $"Prediction: {p.PredictedLabel}");
 
             // Expected output:
             //   Label: 1, Prediction: 1
@@ -58,7 +69,9 @@ public static void Example()
             //   Label: 3, Prediction: 3
 
             // Evaluate the overall metrics
-            var metrics = mlContext.MulticlassClassification.Evaluate(transformedTestData);
+            var metrics = mlContext.MulticlassClassification
+                .Evaluate(transformedTestData);
+
             PrintMetrics(metrics);
             
             // Expected output:
@@ -78,8 +91,11 @@ public static void Example()
             //   Precision ||0.9304 |0.9538 |0.8521 |
         }
 
-        // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        // Generates random uniform doubles in [-0.5, 0.5)
+        // range with labels 1, 2 or 3.
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed=0)
+
         {
             var random = new Random(seed);
             float randomFloat() => (float)(random.NextDouble() - 0.5);
@@ -91,13 +107,17 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                 {
                     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()
+                    // 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.
+        // Example with label and 20 feature values. A data set is a collection of
+        // such examples.
         private class DataPoint
         {
             public uint Label { get; set; }
@@ -120,8 +140,11 @@ public static void PrintMetrics(MulticlassClassificationMetrics metrics)
             Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
             Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
             Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
-            Console.WriteLine($"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+            Console.WriteLine(
+                $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+
             Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
         }
     }
 }
+
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaNonCalibrated.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaNonCalibrated.tt
index 5437228267..cd403b1a44 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaNonCalibrated.tt
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaNonCalibrated.tt
@@ -8,7 +8,8 @@ string TrainerOptions = null;
 string OptionsInclude = "";
 string Comments = "";
 bool CacheData = true;
-string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.";
+string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5)"
+    + "\n        // range with labels 1, 2 or 3.";
 
 string ExpectedOutputPerInstance = @"// Expected output:
             //   Label: 1, Prediction: 1
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaNonCalibratedWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaNonCalibratedWithOptions.cs
index 2577756d9b..6367e9fe21 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaNonCalibratedWithOptions.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaNonCalibratedWithOptions.cs
@@ -11,22 +11,26 @@ public static class SdcaNonCalibratedWithOptions
     {
         public static void Example()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
-            // as a catalog of available operations and as the source of randomness.
-            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
 
             // Create a list of training data points.
             var dataPoints = GenerateRandomDataPoints(1000);
 
-            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            // Convert the list of data points to an IDataView object, which is
+            // consumable by ML.NET API.
             var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
 
-            // ML.NET doesn't cache data set by default. Therefore, if one reads a data set from a file and accesses it many times,
-			// it can be slow due to expensive featurization and disk operations. When the considered data can fit into memory,
-			// a solution is to cache the data in memory. Caching is especially helpful when working with iterative algorithms 
-			// which needs many data passes.
-			trainingData = mlContext.Data.Cache(trainingData);
+            // ML.NET doesn't cache data set by default. Therefore, if one reads a
+            // data set from a file and accesses it many times, it can be slow due
+            // to expensive featurization and disk operations. When the considered
+            // data can fit into memory, a solution is to cache the data in memory.
+            // Caching is especially helpful when working with iterative algorithms 
+            // which needs many data passes.
+            trainingData = mlContext.Data.Cache(trainingData);
 
             // Define trainer options.
             var options = new SdcaNonCalibratedMulticlassTrainer.Options
@@ -39,27 +43,32 @@ public static void Example()
 
             // Define the trainer.
             var pipeline = 
-			        // Convert the string labels into key types.
-                    mlContext.Transforms.Conversion.MapValueToKey("Label")
-                    // Apply SdcaNonCalibrated multiclass trainer.
-                    .Append(mlContext.MulticlassClassification.Trainers.SdcaNonCalibrated(options));
-			
+                // Convert the string labels into key types.
+                mlContext.Transforms.Conversion.MapValueToKey("Label")
+                // Apply SdcaNonCalibrated multiclass trainer.
+                .Append(mlContext.MulticlassClassification.Trainers
+                .SdcaNonCalibrated(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));
+            // 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();
+            var predictions = mlContext.Data
+                .CreateEnumerable<Prediction>(transformedTestData,
+                reuseRowObject: false).ToList();
 
             // Look at 5 predictions
             foreach (var p in predictions.Take(5))
-                Console.WriteLine($"Label: {p.Label}, Prediction: {p.PredictedLabel}");
+                Console.WriteLine($"Label: {p.Label}, " + 
+                    $"Prediction: {p.PredictedLabel}");
 
             // Expected output:
             //   Label: 1, Prediction: 1
@@ -69,7 +78,9 @@ public static void Example()
             //   Label: 3, Prediction: 3
 
             // Evaluate the overall metrics
-            var metrics = mlContext.MulticlassClassification.Evaluate(transformedTestData);
+            var metrics = mlContext.MulticlassClassification
+                .Evaluate(transformedTestData);
+
             PrintMetrics(metrics);
             
             // Expected output:
@@ -89,8 +100,11 @@ public static void Example()
             //   Precision ||0.9236 |0.9591 |0.8372 |
         }
 
-        // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        // Generates random uniform doubles in [-0.5, 0.5)
+        // range with labels 1, 2 or 3.
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed=0)
+
         {
             var random = new Random(seed);
             float randomFloat() => (float)(random.NextDouble() - 0.5);
@@ -102,13 +116,17 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                 {
                     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()
+                    // 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.
+        // Example with label and 20 feature values. A data set is a collection of
+        // such examples.
         private class DataPoint
         {
             public uint Label { get; set; }
@@ -131,8 +149,11 @@ public static void PrintMetrics(MulticlassClassificationMetrics metrics)
             Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
             Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
             Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
-            Console.WriteLine($"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+            Console.WriteLine(
+                $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+
             Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
         }
     }
 }
+
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaNonCalibratedWithOptions.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaNonCalibratedWithOptions.tt
index 19f7ee9dd9..5f0f2a4775 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaNonCalibratedWithOptions.tt
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaNonCalibratedWithOptions.tt
@@ -14,7 +14,8 @@ string TrainerOptions = @"SdcaNonCalibratedMulticlassTrainer.Options
 string OptionsInclude = "using Microsoft.ML.Trainers;";
 string Comments = "";
 bool CacheData = true;
-string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.";
+string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5)"
+    + "\n        // range with labels 1, 2 or 3.";
 
 string ExpectedOutputPerInstance = @"// Expected output:
             //   Label: 1, Prediction: 1