PR feedback.

Author: codemzs

src/Microsoft.ML.FastTree/FastTreeRanking.cs

@@ -52,35 +52,16 @@ namespace Microsoft.ML.Trainers.FastTree
     /// |  |  |
     /// | -- | -- |
     /// | Machine learning task | Ranking |
-    /// | Is normalization required? | Yes |
+    /// | Is normalization required? | No |
     /// | Is caching required? | No |
     /// | Required NuGet in addition to Microsoft.ML | Microsoft.ML.FastTree |
     ///
-    /// ### Training Algorithm Details
-    /// FastTree is an efficient implementation of the [MART](https://arxiv.org/abs/1505.01866) gradient boosting algorithm.
-    /// Gradient boosting is a machine learning technique for regression problems.
-    /// It builds each regression tree in a step-wise fashion, using a predefined loss function to measure the error for each step and corrects for it in the next.
-    /// So this prediction model is actually an ensemble of weaker prediction models.In regression problems, boosting builds a series of such trees in a step-wise fashion and then selects the optimal tree using an arbitrary differentiable loss function.
-    ///
-    /// MART learns an ensemble of regression trees, which is a decision tree with scalar values in its leaves.
-    /// A decision(or regression) tree is a binary tree-like flow chart, where at each interior node one decides which of the two child nodes to continue to based on one of the feature values from the input.
-    /// At each leaf node, a value is returned.In the interior nodes, the decision is based on the test 'x <= v' where x is the value of the feature in the input sample and v is one of the possible values of this feature.
-    /// The functions that can be produced by a regression tree are all the piece-wise constant functions.
-    ///
-    /// The ensemble of trees is produced by computing, in each step, a regression tree that approximates the gradient of the loss function, and adding it to the previous tree with coefficients that minimize the loss of the new tree.
-    /// The output of the ensemble produced by MART on a given instance is the sum of the tree outputs.
-    /// In case of a binary classification problem, the output is converted to a probability by using some form of calibration.
-    /// In case of a regression problem, the output is the predicted value of the function.
-    /// In case of a ranking problem, the instances are ordered by the output value of the ensemble.
-    ///
-    /// For more information see:
-    /// [Wikipedia: Gradient boosting (Gradient tree boosting)](https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting).
-    /// [Greedy function approximation: A gradient boosting machine.](https://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aos/1013203451)
+    /// [!include[algorithm](~/../docs/samples/docs/api-reference/algo-details-fasttree.md)]
     /// ]]>
     /// </format>
     /// </remarks>
-    /// <seealso cref="Microsoft.ML.TreeExtensions.FastTree(Microsoft.ML.RankingCatalog.RankingTrainers,System.String,System.String,System.String,System.String,System.Int32,System.Int32,System.Int32,System.Double)"/>
-    /// <seealso cref="Microsoft.ML.TreeExtensions.FastTree(Microsoft.ML.RankingCatalog.RankingTrainers,Microsoft.ML.Trainers.FastTree.FastTreeRankingTrainer.Options)"/>
+    /// <seealso cref="TreeExtensions.FastTree(RankingCatalog.RankingTrainers, string, string, string, string, int, int, int, double)"/>
+    /// <seealso cref="TreeExtensions.FastTree(RegressionCatalog.RegressionTrainers, FastTreeRegressionTrainer.Options)"/>
     /// <seealso cref="Options"/>
     public sealed partial class FastTreeRankingTrainer
         : BoostingFastTreeTrainerBase<FastTreeRankingTrainer.Options, RankingPredictionTransformer<FastTreeRankingModelParameters>, FastTreeRankingModelParameters>

src/Microsoft.ML.FastTree/TreeTrainersCatalog.cs

@@ -122,11 +122,11 @@ public static FastTreeBinaryTrainer FastTree(this BinaryClassificationCatalog.Bi
         }
 
         /// <summary>
-        /// Creates a <see cref="FastTreeRankingTrainer"/>, which ranks a series of inputs based on their relevance and training a decision tree ranking model.
+        /// Create a <see cref="FastTreeRankingTrainer"/>, which ranks a series of inputs based on their relevance and training a decision tree ranking model.
         /// </summary>
         /// <param name="catalog">The <see cref="RankingCatalog"/>.</param>
         /// <param name="labelColumnName">The name of the label column. The column data must be <see cref="System.Single"/>.</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="featureColumnName">The name of the feature column. The column data must be a known-sized vector of <see cref="System.Single"/>.</param>
         /// <param name="rowGroupColumnName">The name of the group column.</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>
@@ -156,7 +156,7 @@ public static FastTreeRankingTrainer FastTree(this RankingCatalog.RankingTrainer
         }
 
         /// <summary>
-        /// Creates a <see cref="FastTreeRankingTrainer"/>, which ranks a series of inputs based on their relevance and training a decision tree ranking model with advanced options.
+        /// Create a <see cref="FastTreeRankingTrainer"/> with advanced options, which ranks a series of inputs based on their relevance and training a decision tree ranking model.
         /// </summary>
         /// <param name="catalog">The <see cref="RankingCatalog"/>.</param>
         /// <param name="options">Trainer options.</param>