Address Ivan's comments (hopefully)

Author: wschin

docs/samples/Microsoft.ML.Samples/Dynamic/Calibrator.cs

@@ -49,7 +49,7 @@ public static void Calibration()
             // Then append the StochasticDualCoordinateAscentBinary binary classifier, setting the "Label" column as the label of the dataset, and 
             // the "Features" column produced by FeaturizeText as the features column. 
             var pipeline = mlContext.Transforms.Text.FeaturizeText("SentimentText", "Features")
-                    .Append(mlContext.BinaryClassification.Trainers.StochasticDualCoordinateAscent(
+                    .Append(mlContext.BinaryClassification.Trainers.StochasticDualCoordinateAscentNonCalibrated(
                         labelColumn: "Sentiment", 
                         featureColumn: "Features", 
                         l2Const: 0.001f, 

docs/samples/Microsoft.ML.Samples/Dynamic/SDCACalibrated.cs renamed to docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/SDCA.cs

@@ -5,15 +5,9 @@
 
 namespace Microsoft.ML.Samples.Dynamic
 {
-    public class SDCACalibrated_BinaryClassificationExample
+    public class SDCALogisticRegressionExample
     {
-        /// <summary>
-        /// Call <see cref="SdcaCalibratedBinaryTrainer"/> to train a logistic regression model.
-        /// Training examples are generated by calling <see cref="Samples.Utils.DatasetUtils.GenerateBinaryLabelFloatFeatureVectorSamples"/>.
-        /// The type of those examples is <see cref="SamplesUtils.DatasetUtils.FloatLabelFloatFeatureVectorSample"/>,
-        /// which contains one bool label and a float feature vector.
-        /// </summary>
-        public static void SDCACalibrated_BinaryClassification()
+        public static void SDCALogisticRegression()
         {
             // Generate C# objects as training examples.
             var rawData = SamplesUtils.DatasetUtils.GenerateBinaryLabelFloatFeatureVectorSamples(100);
@@ -41,13 +35,13 @@ public static void SDCACalibrated_BinaryClassification()
             // Step 1: Read the data as an IDataView.
             var data = mlContext.Data.ReadFromEnumerable(rawData);
 
-            // ML.NET doesn't cache data set by default. Caching is very helpful when working with iterative
-            // algorithms which needs many data passes. Since SDCA is the case, we cache.
+            // ML.NET doesn't cache data set by default. Caching is always recommended when using the
+            // StochasticDualCoordinateAscent algorithm because it may incur multiple data passes.
             data = mlContext.Data.Cache(data);
 
-            // Step 2: Create a binary classifier.
+            // Step 2: Create a binary classifier. This trainer may produce a logistic regression model.
             // We set the "Label" column as the label of the dataset, and the "Features" column as the features column.
-            var pipeline = mlContext.BinaryClassification.Trainers.StochasticDualCoordinateAscentCalibrated(labelColumn: "Label", featureColumn: "Features", l2Const: 0.001f);
+            var pipeline = mlContext.BinaryClassification.Trainers.StochasticDualCoordinateAscent(labelColumn: "Label", featureColumn: "Features", l2Const: 0.001f);
 
             // Step 3: Train the pipeline created.
             var model = pipeline.Fit(data);
@@ -62,7 +56,7 @@ public static void SDCACalibrated_BinaryClassification()
 
             Console.WriteLine("The first example actual label is {0}." +
                 " The trained model assigns it a score {1} and a probability of being positive class {2}.",
-                first.Label /*true*/, first.Score /*around 3.2*/, first.Probability /*around around 0.95*/);
+                first.Label /*true*/, first.Score /*around 3.2*/, first.Probability /*around 0.95*/);
         }
     }
 }

docs/samples/Microsoft.ML.Samples/Dynamic/SDCA.cs renamed to docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/SDCANonCalibrated.cs

@@ -5,9 +5,9 @@
 
 namespace Microsoft.ML.Samples.Dynamic
 {
-    public class SDCA_BinaryClassificationExample
+    public class SDCABinaryClassificationExample
     {
-        public static void SDCA_BinaryClassification()
+        public static void SDCABinaryClassification()
         {
             // Downloading the dataset from github.com/dotnet/machinelearning.
             // This will create a sentiment.tsv file in the filesystem.
@@ -49,7 +49,7 @@ public static void SDCA_BinaryClassification()
             // the "Features" column produced by FeaturizeText as the features column.
             var pipeline = mlContext.Transforms.Text.FeaturizeText("SentimentText", "Features")
                     .AppendCacheCheckpoint(mlContext) // Add a data-cache step within a pipeline.
-                    .Append(mlContext.BinaryClassification.Trainers.StochasticDualCoordinateAscent(labelColumn: "Sentiment", featureColumn: "Features", l2Const: 0.001f));
+                    .Append(mlContext.BinaryClassification.Trainers.StochasticDualCoordinateAscentNonCalibrated(labelColumn: "Sentiment", featureColumn: "Features", l2Const: 0.001f));
 
             // Step 3: Run Cross-Validation on this pipeline.
             var cvResults = mlContext.BinaryClassification.CrossValidate(data, pipeline, labelColumn: "Sentiment");
@@ -60,8 +60,8 @@ public static void SDCA_BinaryClassification()
             // If we wanted to specify more advanced parameters for the algorithm, 
             // we could do so by tweaking the 'advancedSetting'.
             var advancedPipeline = mlContext.Transforms.Text.FeaturizeText("SentimentText", "Features")
-                                  .Append(mlContext.BinaryClassification.Trainers.StochasticDualCoordinateAscent(
-                                      new SdcaBinaryTrainer.Options { 
+                                  .Append(mlContext.BinaryClassification.Trainers.StochasticDualCoordinateAscentNonCalibrated(
+                                      new SdcaNonCalibratedBinaryTrainer.Options { 
                                         LabelColumn = "Sentiment",
                                         FeatureColumn = "Features",
                                         ConvergenceTolerance = 0.01f,  // The learning rate for adjusting bias from being regularized

docs/samples/Microsoft.ML.Samples/Static/SDCABinaryClassification.cs

@@ -73,7 +73,7 @@ public static void SdcaBinaryClassification()
                 .Append(row => (
                         Features: row.Features.Normalize(),
                         Label: row.Label,
-                        Score: mlContext.BinaryClassification.Trainers.SdcaCalibrated(
+                        Score: mlContext.BinaryClassification.Trainers.Sdca(
                             row.Label,
                             row.Features,
                             l1Threshold: 0.25f,