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Author: codemzs

src/Microsoft.ML.StandardTrainers/Standard/SdcaBinary.cs

@@ -2170,7 +2170,7 @@ private protected override void CheckLabel(RoleMappedData examples, out int weig
     }
 
     /// <summary>
-    /// The<see cref="IEstimator{TTransformer}"/> for training logistic regression using a parallel stochastic gradient method.
+    /// The <see cref="IEstimator{TTransformer}"/> for training logistic regression using a parallel stochastic gradient method.
     /// The trained model is <a href='https://en.wikipedia.org/wiki/Calibration_(statistics)'>calibrated</a> and can produce probability by feeding the output value of the
     /// linear function to a <see cref="PlattCalibrator"/>.
     /// </summary>
@@ -2179,7 +2179,7 @@ private protected override void CheckLabel(RoleMappedData examples, out int weig
     /// To create this trainer, use [SgdCalibrated](xref:Microsoft.ML.StandardTrainersCatalog.SgdCalibrated(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,System.String,System.String,System.String,System.Int32,System.Double,System.Single))
     /// or [SgdCalibrated(Options)](xref:Microsoft.ML.StandardTrainersCatalog.SgdCalibrated(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,Microsoft.ML.Trainers.SgdCalibratedTrainer.Options)).
     ///
-    /// [!include[io](~/../docs/samples/docs/api-reference/io-columns-binary-classification.md)]
+    /// [!include[io](~/../docs/samples/docs/api-reference/io-columns-regression.md)]
     ///
     /// ### Trainer Characteristics
     /// |  |  |
@@ -2191,14 +2191,14 @@ private protected override void CheckLabel(RoleMappedData examples, out int weig
     ///
     /// ### Training Algorithm Details
     /// The Stochastic Gradient Descent (SGD) is one of the popular stochastic optimization procedures that can be integrated
-    /// into several machine learning tasks to achieve state-of-the-art performance. This trainer implements the Hogwild SGD for binary classification
-    /// that supports multi-threading without any locking. If the associated optimization problem is sparse, Hogwild SGD achieves a nearly optimal
-    /// rate of convergence. For more details about Hogwild SGD can be found [here](http://arxiv.org/pdf/1106.5730v2.pdf).
+    /// into several machine learning tasks to achieve state-of-the-art performance. This trainer implements the Hogwild Stochastic Gradient Descent for binary classification
+    /// that supports multi-threading without any locking. If the associated optimization problem is sparse, Hogwild Stochastic Gradient Descent achieves a nearly optimal
+    /// rate of convergence. For more details about Hogwild Stochastic Gradient Descent can be found [here](http://arxiv.org/pdf/1106.5730v2.pdf).
     /// ]]>
     /// </format>
     /// </remarks>
-    /// <seealso cref="Microsoft.ML.StandardTrainersCatalog.SgdCalibrated(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,System.String,System.String,System.String,System.Int32,System.Double,System.Single)"/>
-    /// <seealso cref="Microsoft.ML.StandardTrainersCatalog.SgdCalibrated(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,Microsoft.ML.Trainers.SgdCalibratedTrainer.Options)"/>
+    /// <seealso cref="StandardTrainersCatalog.SgdCalibrated(BinaryClassificationCatalog.BinaryClassificationTrainers, string, string, string, int, double, float)"/>
+    /// <seealso cref="StandardTrainersCatalog.SgdCalibrated(BinaryClassificationCatalog.BinaryClassificationTrainers, SgdCalibratedTrainer.Options)"/>
     /// <seealso cref="Options"/>
     public sealed class SgdCalibratedTrainer :
         SgdBinaryTrainerBase<CalibratedModelParametersBase<LinearBinaryModelParameters, PlattCalibrator>>
@@ -2269,7 +2269,7 @@ private protected override CalibratedModelParametersBase<LinearBinaryModelParame
     /// To create this trainer, use [SgdNonCalibrated](xref:Microsoft.ML.StandardTrainersCatalog.SgdNonCalibrated(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,System.String,System.String,System.String,Microsoft.ML.Trainers.IClassificationLoss,System.Int32,System.Double,System.Single))
     /// or [SgdNonCalibrated(Options)](xref:Microsoft.ML.StandardTrainersCatalog.SgdNonCalibrated(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,Microsoft.ML.Trainers.SgdNonCalibratedTrainer.Options)).
     ///
-    /// [!include[io](~/../docs/samples/docs/api-reference/io-columns-binary-classification.md)]
+    /// [!include[io](~/../docs/samples/docs/api-reference/io-columns-regression.md)]
     ///
     /// ### Trainer Characteristics
     /// |  |  |
@@ -2280,15 +2280,15 @@ private protected override CalibratedModelParametersBase<LinearBinaryModelParame
     /// | Required NuGet in addition to Microsoft.ML | None |
     ///
     /// ### Training Algorithm Details
-    /// The Stochastic Gradient Descent (SGD) is one of the popular stochastic optimization procedures that can be integrated
-    /// into several machine learning tasks to achieve state-of-the-art performance. This trainer implements the Hogwild SGD for binary classification
-    /// that supports multi-threading without any locking. If the associated optimization problem is sparse, Hogwild SGD achieves a nearly optimal
-    /// rate of convergence. For more details about Hogwild SGD can be found [here](http://arxiv.org/pdf/1106.5730v2.pdf).
+    /// The Stochastic Gradient Descent is one of the popular stochastic optimization procedures that can be integrated
+    /// into several machine learning tasks to achieve state-of-the-art performance. This trainer implements the Hogwild Stochastic Gradient Descent for binary classification
+    /// that supports multi-threading without any locking. If the associated optimization problem is sparse, Hogwild Stochastic Gradient Descent achieves a nearly optimal
+    /// rate of convergence. For more details about Hogwild Stochastic Gradient Descent can be found [here](http://arxiv.org/pdf/1106.5730v2.pdf).
     /// ]]>
     /// </format>
     /// </remarks>
-    /// <seealso cref="Microsoft.ML.StandardTrainersCatalog.SgdNonCalibrated(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,System.String,System.String,System.String,Microsoft.ML.Trainers.IClassificationLoss,System.Int32,System.Double,System.Single)"/>
-    /// <seealso cref="Microsoft.ML.StandardTrainersCatalog.SgdNonCalibrated(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,Microsoft.ML.Trainers.SgdNonCalibratedTrainer.Options)"/>
+    /// <seealso cref="StandardTrainersCatalog.SgdNonCalibrated(BinaryClassificationCatalog.BinaryClassificationTrainers, string, string, string, IClassificationLoss, int, double, float)"/>
+    /// <seealso cref="StandardTrainersCatalog.SgdNonCalibrated(BinaryClassificationCatalog.BinaryClassificationTrainers, SgdNonCalibratedTrainer.Options)"/>
     /// <seealso cref="Options"/>
     public sealed class SgdNonCalibratedTrainer :
         SgdBinaryTrainerBase<LinearBinaryModelParameters>

src/Microsoft.ML.StandardTrainers/StandardTrainersCatalog.cs

@@ -17,12 +17,12 @@ namespace Microsoft.ML
     public static class StandardTrainersCatalog
     {
         /// <summary>
-        /// Creates a <see cref="Trainers.SgdCalibratedTrainer"/> that predicts a target using a linear classification model.
+        /// Create <see cref="SgdCalibratedTrainer"/>, which predicts a target using a linear classification model.
         /// Stochastic gradient descent (SGD) is an iterative algorithm that optimizes a differentiable objective function.
         /// </summary>
         /// <param name="catalog">The binary classification catalog trainer object.</param>
         /// <param name="labelColumnName">The name of the label column, or dependent variable. The column data must be <see cref="System.Boolean"/>.</param>
-        /// <param name="featureColumnName">The features, or independent variables. The column data must be a known-sized vector of <see cref="System.Single"/></param>
+        /// <param name="featureColumnName">The features, or independent variables. The column data must be a known-sized vector of <see cref="System.Single"/>.</param>
         /// <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
         /// <param name="numberOfIterations">The maximum number of passes through the training dataset; set to 1 to simulate online learning.</param>
         /// <param name="learningRate">The initial learning rate used by SGD.</param>
@@ -49,7 +49,7 @@ public static SgdCalibratedTrainer SgdCalibrated(this BinaryClassificationCatalo
         }
 
         /// <summary>
-        /// Creates a <see cref="Trainers.SgdCalibratedTrainer"/> that predicts a target using a linear classification model and advanced options.
+        /// Create <see cref="Trainers.SgdCalibratedTrainer"/> with advanced options, which predicts a target using a linear classification model.
         /// Stochastic gradient descent (SGD) is an iterative algorithm that optimizes a differentiable objective function.
         /// </summary>
         /// <param name="catalog">The binary classification catalog trainer object.</param>
@@ -72,12 +72,12 @@ public static SgdCalibratedTrainer SgdCalibrated(this BinaryClassificationCatalo
         }
 
         /// <summary>
-        /// Creates a <see cref="Trainers.SgdNonCalibratedTrainer"/> that predicts a target using a linear classification model.
+        /// Create <see cref="Trainers.SgdNonCalibratedTrainer"/>, which predicts a target using a linear classification model.
         /// Stochastic gradient descent (SGD) is an iterative algorithm that optimizes a differentiable objective function.
         /// </summary>
         /// <param name="catalog">The binary classification catalog trainer object.</param>
         /// <param name="labelColumnName">The name of the label column, or dependent variable. The column data must be <see cref="System.Boolean"/>.</param>
-        /// <param name="featureColumnName">The features, or independent variables. The column data must be a known-sized vector of <see cref="System.Single"/></param>
+        /// <param name="featureColumnName">The features, or independent variables. The column data must be a known-sized vector of <see cref="System.Single"/>.</param>
         /// <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
         /// <param name="lossFunction">The <a href="https://en.wikipedia.org/wiki/Loss_function">loss</a> function minimized in the training process. Using, for example, <see cref="HingeLoss"/> leads to a support vector machine trainer.</param>
         /// <param name="numberOfIterations">The maximum number of passes through the training dataset; set to 1 to simulate online learning.</param>
@@ -106,7 +106,7 @@ public static SgdNonCalibratedTrainer SgdNonCalibrated(this BinaryClassification
         }
 
         /// <summary>
-        /// Creates a <see cref="Trainers.SgdNonCalibratedTrainer"/> that predicts a target using a linear classification model and advanced options.
+        /// Create <see cref="Trainers.SgdNonCalibratedTrainer"/> with advanced options, which predicts a target using a linear classification model.
         /// Stochastic gradient descent (SGD) is an iterative algorithm that optimizes a differentiable objective function.
         /// </summary>
         /// <param name="catalog">The binary classification catalog trainer object.</param>