XML documentation for LightGBM binary classification. (#3397)

* XML documentation for LightGBM binary classification.

* PR feedback.

Author: codemzs

src/Microsoft.ML.LightGbm/LightGbmBinaryTrainer.cs

@@ -84,7 +84,27 @@ private static IPredictorProducing<float> Create(IHostEnvironment env, ModelLoad
     /// <summary>
     /// The <see cref="IEstimator{TTransformer}"/> for training a boosted decision tree binary classification model using LightGBM.
     /// </summary>
-    /// <include file='doc.xml' path='doc/members/member[@name="LightGBM_remarks"]/*' />
+    /// <remarks>
+    /// <format type="text/markdown"><![CDATA[
+    /// To create this trainer, use [LightGbm](xref:Microsoft.ML.LightGbmExtensions.LightGbm(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,System.String,System.String,System.String,System.Nullable{System.Int32},System.Nullable{System.Int32},System.Nullable{System.Double},System.Int32)) or
+    /// [LightGbm](xref:Microsoft.ML.LightGbmExtensions.LightGbm(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,Microsoft.ML.Trainers.LightGbm.LightGbmBinaryTrainer.Options)).
+    ///
+    /// [!include[io](~/../docs/samples/docs/api-reference/io-columns-binary-classification.md)]
+    ///
+    /// ### Trainer Characteristics
+    /// |  |  |
+    /// | -- | -- |
+    /// | Machine learning task | Binary classification |
+    /// | Is normalization required? | No |
+    /// | Is caching required? | No |
+    /// | Required NuGet in addition to Microsoft.ML | Microsoft.ML.FastTree |
+    ///
+    /// [!include[algorithm](~/../docs/samples/docs/api-reference/algo-details-lightgbm.md)]
+    /// ]]>
+    /// </format>
+    /// </remarks>
+    /// <seealso cref="LightGbmExtensions.LightGbm(BinaryClassificationCatalog.BinaryClassificationTrainers, string, string, string, int?, int?, double?, int)"/>
+    /// <seealso cref="LightGbmExtensions.LightGbm(BinaryClassificationCatalog.BinaryClassificationTrainers, LightGbmBinaryTrainer.Options)"/>
     public sealed class LightGbmBinaryTrainer : LightGbmTrainerBase<LightGbmBinaryTrainer.Options, float,
         BinaryPredictionTransformer<CalibratedModelParametersBase<LightGbmBinaryModelParameters, PlattCalibrator>>,
         CalibratedModelParametersBase<LightGbmBinaryModelParameters, PlattCalibrator>>

src/Microsoft.ML.LightGbm/LightGbmCatalog.cs

@@ -66,11 +66,11 @@ public static LightGbmRegressionTrainer LightGbm(this RegressionCatalog.Regressi
         }
 
         /// <summary>
-        /// Predict a target using a gradient boosting decision tree binary classification model trained with the <see cref="LightGbmBinaryTrainer"/>.
+        /// Create <see cref="LightGbmBinaryTrainer"/>, which predicts a target using a gradient boosting decision tree binary classification.
         /// </summary>
         /// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
-        /// <param name="labelColumnName">The name of the label column.</param>
-        /// <param name="featureColumnName">The name of the feature column.</param>
+        /// <param name="labelColumnName">The name of the label column. The column data must be <see cref="System.Boolean"/>.</param>
+        /// <param name="featureColumnName">The name of the feature column. 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="numberOfLeaves">The maximum number of leaves in one tree.</param>
         /// <param name="minimumExampleCountPerLeaf">The minimal number of data points required to form a new tree leaf.</param>
@@ -98,7 +98,7 @@ public static LightGbmBinaryTrainer LightGbm(this BinaryClassificationCatalog.Bi
         }
 
         /// <summary>
-        /// Predict a target using a gradient boosting decision tree binary classification model trained with the <see cref="LightGbmBinaryTrainer"/> and advanced options.
+        /// Create <see cref="LightGbmBinaryTrainer"/> with advanced options, which predicts a target using a gradient boosting decision tree binary classification.
         /// </summary>
         /// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
         /// <param name="options">Trainer options.</param>