diff --git a/src/Microsoft.ML.FastTree/FastTreeArguments.cs b/src/Microsoft.ML.FastTree/FastTreeArguments.cs
index 08d9a828d4..9a206d442f 100644
--- a/src/Microsoft.ML.FastTree/FastTreeArguments.cs
+++ b/src/Microsoft.ML.FastTree/FastTreeArguments.cs
@@ -155,7 +155,8 @@ public Options()
     public sealed partial class FastTreeTweedieTrainer
     {
         /// <summary>
-        /// Options for the <see cref="FastTreeTweedieTrainer"/>.
+        /// Options for the <see cref="FastTreeTweedieTrainer"/> as used in
+        /// [FastTreeTweedie(Options)](xref:Microsoft.ML.TreeExtensions.FastTreeTweedie(Microsoft.ML.RegressionCatalog.RegressionTrainers,Microsoft.ML.Trainers.FastTree.FastTreeTweedieTrainer.Options)).
         /// </summary>
         [TlcModule.Component(Name = LoadNameValue, FriendlyName = UserNameValue, Desc = Summary)]
         public sealed class Options : BoostedTreeOptions, IFastTreeTrainerFactory
diff --git a/src/Microsoft.ML.FastTree/FastTreeTweedie.cs b/src/Microsoft.ML.FastTree/FastTreeTweedie.cs
index a738e14134..7efa9424c0 100644
--- a/src/Microsoft.ML.FastTree/FastTreeTweedie.cs
+++ b/src/Microsoft.ML.FastTree/FastTreeTweedie.cs
@@ -30,12 +30,32 @@ namespace Microsoft.ML.Trainers.FastTree
     /// This trainer is a generalization of Poisson, compound Poisson, and gamma regression.
     /// </summary>
     /// <remarks>
-    /// The Tweedie boosting model follows the mathematics established in <a href="https://arxiv.org/pdf/1508.06378.pdf">
-    /// Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models</a> from Yang, Quan, and Zou.
+    /// <format type="text/markdown"><![CDATA[
+    /// To create this trainer, use [FastTreeTweedie](xref:Microsoft.ML.TreeExtensions.FastTreeTweedie(Microsoft.ML.RegressionCatalog.RegressionTrainers,System.String,System.String,System.String,System.Int32,System.Int32,System.Int32,System.Double))
+    /// or [FastTreeTweedie(Options)](xref:Microsoft.ML.TreeExtensions.FastTreeTweedie(Microsoft.ML.RegressionCatalog.RegressionTrainers,Microsoft.ML.Trainers.FastTree.FastTreeTweedieTrainer.Options)).
+    ///
+    /// [!include[io](~/../docs/samples/docs/api-reference/io-columns-regression.md)]
+    ///
+    /// ### Trainer Characteristics
+    /// |  |  |
+    /// | -- | -- |
+    /// | Machine learning task | Regression |
+    /// | Is normalization required? | No |
+    /// | Is caching required? | No |
+    /// | Required NuGet in addition to Microsoft.ML | Microsoft.ML.FastTree |
+    ///
+    /// ### Training Algorithm Details
+    /// The Tweedie boosting model follows the mathematics established in
+    /// [Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models</a> from Yang, Quan, and Zou](https://arxiv.org/pdf/1508.06378.pdf).
     /// For an introduction to Gradient Boosting, and more information, see:
-    /// <a href='https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting'> Wikipedia: Gradient boosting(Gradient tree boosting)</a> or
-    /// <a href='https://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aos/1013203451'> Greedy function approximation: A gradient boosting machine</a>.
+    /// [Wikipedia: Gradient boosting(Gradient tree boosting)](https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting) or
+    /// [Greedy function approximation: A gradient boosting machine](https://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aos/1013203451).
+    /// ]]>
+    /// </format>
     /// </remarks>
+    /// <seealso cref="TreeExtensions.FastTreeTweedie(RegressionCatalog.RegressionTrainers, string, string, string, int, int, int, double)"/>
+    /// <seealso cref="TreeExtensions.FastTreeTweedie(RegressionCatalog.RegressionTrainers, FastTreeTweedieTrainer.Options)"/>
+    /// <seealso cref="Options"/>
     public sealed partial class FastTreeTweedieTrainer
          : BoostingFastTreeTrainerBase<FastTreeTweedieTrainer.Options, RegressionPredictionTransformer<FastTreeTweedieModelParameters>, FastTreeTweedieModelParameters>
     {
diff --git a/src/Microsoft.ML.FastTree/TreeTrainersCatalog.cs b/src/Microsoft.ML.FastTree/TreeTrainersCatalog.cs
index 987abb2ac6..b989c11b4a 100644
--- a/src/Microsoft.ML.FastTree/TreeTrainersCatalog.cs
+++ b/src/Microsoft.ML.FastTree/TreeTrainersCatalog.cs
@@ -278,11 +278,11 @@ public static GamRegressionTrainer Gam(this RegressionCatalog.RegressionTrainers
         }
 
         /// <summary>
-        /// Predict a target using a decision tree regression model trained with the <see cref="FastTreeTweedieTrainer"/>.
+        /// Create <see cref="FastTreeTweedieTrainer"/>, which predicts a target using a decision tree regression model.
         /// </summary>
         /// <param name="catalog">The <see cref="RegressionCatalog"/>.</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.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="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>
@@ -310,7 +310,7 @@ public static FastTreeTweedieTrainer FastTreeTweedie(this RegressionCatalog.Regr
         }
 
         /// <summary>
-        /// Predict a target using a decision tree regression model trained with the <see cref="FastTreeTweedieTrainer"/> and advanced options.
+        /// Create <see cref="FastTreeTweedieTrainer"/> using advanced options, which predicts a target using a decision tree regression model.
         /// </summary>
         /// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
         /// <param name="options">Trainer options.</param>