Reference to See Also section for example of usage in all estimators (#3577)

Author: artidoro

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

@@ -28,4 +28,6 @@ For more see:
 * [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/)
+  Forests](https://blogs.technet.microsoft.com/machinelearning/2014/09/10/from-stumps-to-trees-to-forests/)
+
+Check the See Also section for links to examples of the usage.

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

@@ -35,4 +35,6 @@ 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)
+machine.](https://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aos/1013203451)
+
+Check the See Also section for links to examples of the usage.

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

@@ -15,4 +15,6 @@ the average prediction over the training set, and the shape functions are
 normalized to represent the deviation from the average prediction. This results
 in models that are easily interpreted simply by inspecting the intercept and the
 shape functions. See the sample below for an example of how to train a GAM model
-and inspect and interpret the results.
+and inspect and interpret the results.
+
+Check the See Also section for links to examples of the usage.

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

@@ -3,3 +3,5 @@ LightGBM is an open source implementation of gradient boosting decision tree.
 For implementation details, please see [LightGBM's official
 documentation](https://lightgbm.readthedocs.io/en/latest/index.html) or this
 [paper](https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree.pdf).
+
+Check the See Also section for links to examples of the usage.

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

@@ -57,4 +57,6 @@ For more information, see:
 * [Scaling Up Stochastic Dual Coordinate
   Ascent.](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/main-3.pdf)
 * [Stochastic Dual Coordinate Ascent Methods for Regularized Loss
-  Minimization.](http://www.jmlr.org/papers/volume14/shalev-shwartz13a/shalev-shwartz13a.pdf)
+  Minimization.](http://www.jmlr.org/papers/volume14/shalev-shwartz13a/shalev-shwartz13a.pdf)
+
+Check the See Also section for links to examples of the usage.

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

@@ -6,4 +6,6 @@ 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).
+found [here](http://arxiv.org/pdf/1106.5730v2.pdf).
+
+Check the See Also section for links to examples of the usage.

src/Microsoft.ML.Data/Transforms/ColumnConcatenatingEstimator.cs

@@ -31,7 +31,7 @@ namespace Microsoft.ML.Transforms
     /// If the input columns' data type is a vector the output column data type remains the same. However, the size of
     /// the vector will be the sum of the sizes of the input vectors.
     ///
-    /// Check the See Also section for links to examples of the usage.
+    /// Check the See Also section for links to usage examples.
     /// ]]></format>
     /// </remarks>
     /// <seealso cref="TransformExtensionsCatalog.Concatenate(TransformsCatalog, string, string[])"/>

src/Microsoft.ML.Data/Transforms/ColumnCopying.cs

@@ -45,7 +45,7 @@ namespace Microsoft.ML.Transforms
     ///
     /// The resulting [ColumnCopyingTransformer](xref:Microsoft.ML.Transforms.ColumnCopyingTransformer) creates a new column, named as specified in the output column name parameters, and
     /// copies the data from the input column to this new column.
-    /// Check the See Also section for links to examples of the usage.
+    /// Check the See Also section for links to usage examples.
     /// ]]>
     /// </format>
     /// </remarks>

src/Microsoft.ML.Data/Transforms/ColumnSelecting.cs

@@ -53,7 +53,7 @@ namespace Microsoft.ML.Transforms
     /// In the case of serialization, every column in the schema will be written out. If there are columns
     /// that should not be saved, this estimator can be used to remove them.
     ///
-    /// Check the See Also section for links to examples of the usage.
+    /// Check the See Also section for links to usage examples.
     /// ]]></format>
     /// </remarks>
     /// <seealso cref="TransformExtensionsCatalog.DropColumns(TransformsCatalog, string[])"/>

src/Microsoft.ML.Data/Transforms/FeatureContributionCalculationTransformer.cs

@@ -284,6 +284,8 @@ private Delegate GetValueGetter<TSrc>(DataViewRow input, int colSrc)
     /// while keeping the other features constant. The contribution of feature F1 for the given example is the difference between the original score
     /// and the score obtained by taking the opposite decision at the node corresponding to feature F1. This algorithm extends naturally to models with
     /// many decision trees.
+    ///
+    /// Check the See Also section for links to usage examples.
     /// ]]></format>
     /// </remarks>
     /// <seealso cref="ExplainabilityCatalog.CalculateFeatureContribution(TransformsCatalog, ISingleFeaturePredictionTransformer{ICalculateFeatureContribution}, int, int, bool)"/>