XML documentation for FastForest binary classification.

docs/api-reference/algo-details-fastforest.md

@@ -0,0 +1,31 @@
+### Training Algorithm Details
+Decision trees are non-parametric models that perform a sequence of simple tests
+on inputs. This decision procedure maps them to outputs found in the training
+dataset whose inputs were similar to the instance being processed. A decision is
+made at each node of the binary tree data structure based on a measure of
+similarity that maps each instance recursively through the branches of the tree
+until the appropriate leaf node is reached and the output decision returned.
+
+Decision trees have several advantages:
+* They are efficient in both computation and memory usage during training and
+  prediction.
+* They can represent non-linear decision boundaries.
+* They perform integrated feature selection and classification.
+* They are resilient in the presence of noisy features.
+
+Fast forest is a random forest implementation. The model consists of an ensemble
+of decision trees. Each tree in a decision forest outputs a Gaussian
+distribution by way of prediction. An aggregation is performed over the ensemble
+of trees to find a Gaussian distribution closest to the combined distribution
+for all trees in the model. This decision forest classifier consists of an
+ensemble of decision trees.
+
+Generally, ensemble models provide better coverage and accuracy than single
+decision trees. Each tree in a decision forest outputs a Gaussian distribution.
+
+For more see:
+* [Wikipedia: Random forest](https://en.wikipedia.org/wiki/Random_forest)
+* [Quantile regression
+  forest](http://jmlr.org/papers/volume7/meinshausen06a/meinshausen06a.pdf)
+* [From Stumps to Trees to
+  Forests](https://blogs.technet.microsoft.com/machinelearning/2014/09/10/from-stumps-to-trees-to-forests/)

src/Microsoft.ML.FastTree/RandomForestClassification.cs

@@ -113,12 +113,26 @@ private static IPredictorProducing<float> Create(IHostEnvironment env, ModelLoad
     /// <summary>
     /// The <see cref="IEstimator{TTransformer}"/> for training a decision tree binary classification model using Fast Forest.
     /// </summary>
-    /// <include file='doc.xml' path='doc/members/member[@name="FastForest_remarks"]/*' />
+    /// <remarks>
+    /// <format type="text/markdown"><![CDATA[
+    /// To create this trainer, use [FastForest](xref:Microsoft.ML.TreeExtensions.FastForest(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,System.String,System.String,System.String,System.Int32,System.Int32,System.Int32))
+    /// or [FastForest(Options)](xref:Microsoft.ML.TreeExtensions.FastForest(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,Microsoft.ML.Trainers.FastTree.FastForestBinaryTrainer.Options)).
+    ///
+    /// [!include[io](~/../docs/samples/docs/api-reference/io-columns-binary-classification.md)]
+    ///
+    /// [!include[algorithm](~/../docs/samples/docs/api-reference/algo-details-fastforest.md)]
+    /// ]]>
+    /// </format>
+    /// </remarks>
+    /// <seealso cref="Microsoft.ML.TreeExtensions.FastForest(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,System.String,System.String,System.String,System.Int32,System.Int32,System.Int32)"/>
+    /// <seealso cref="Microsoft.ML.TreeExtensions.FastForest(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,Microsoft.ML.Trainers.FastTree.FastForestBinaryTrainer.Options)"/>
+    /// <seealso cref="Options"/>
     public sealed partial class FastForestBinaryTrainer :
         RandomForestTrainerBase<FastForestBinaryTrainer.Options, BinaryPredictionTransformer<FastForestBinaryModelParameters>, FastForestBinaryModelParameters>
     {
         /// <summary>
-        /// Options for the <see cref="FastForestBinaryTrainer"/>.
+        /// Options for the <see cref="FastForestBinaryTrainer"/> as used in
+        /// [FastForest(Options)](xref:Microsoft.ML.TreeExtensions.FastForest(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,Microsoft.ML.Trainers.FastTree.FastForestBinaryTrainer.Options)).
         /// </summary>
         public sealed class Options : FastForestOptionsBase
         {

src/Microsoft.ML.FastTree/TreeTrainersCatalog.cs

@@ -384,11 +384,11 @@ public static FastForestRegressionTrainer FastForest(this RegressionCatalog.Regr
         }
 
         /// <summary>
-        /// Predict a target using a decision tree regression model trained with the <see cref="FastForestBinaryTrainer"/>.
+        /// Create <see cref="FastForestBinaryTrainer"/>, which predicts a target using a decision tree regression model.
         /// </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="numberOfTrees">Total number of decision trees to create in the ensemble.</param>
         /// <param name="numberOfLeaves">The maximum number of leaves per decision tree.</param>
@@ -414,7 +414,7 @@ public static FastForestBinaryTrainer FastForest(this BinaryClassificationCatalo
         }
 
         /// <summary>
-        /// Predict a target using a decision tree regression model trained with the <see cref="FastForestBinaryTrainer"/> and advanced options.
+        /// Create <see cref="FastForestBinaryTrainer"/> with advanced options, which predicts a target using a decision tree regression model.
         /// </summary>
         /// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
         /// <param name="options">Trainer options.</param>