Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/4709#discussion_r25113939
--- Diff: docs/mllib-feature-extraction.md ---
@@ -375,3 +375,28 @@ data2 = labels.zip(normalizer2.transform(features))
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+
+## Feature selection
+Feature selection allows selecting relevant features for use in model
construction leaving out the redundant ones. The number of features to select
can be determined using the validation set. Feature selection is usually
applied on sparse data, for example in text classification. Feature selection
reduces the size of the vector space and, in turn, the complexity of any
subsequent operation with vectors.
+
+### ChiSqSelector
+ChiSqSelector stands for Chi-Squared feature selection. It operates on the
labeled data. ChiSqSelector orders categorical features based on their values
of Chi-Squared test on independence from class and filters (selects) top given
features.
+
+#### Model Fitting
+
+[`ChiSqSelector`](api/scala/index.html#org.apache.spark.mllib.feature.ChiSqSelector)
has the
+following parameters in the constructor:
+
+* `numTopFeatures` number of top features that selector will select
(filter).
+
+We provide a
[`fit`](api/scala/index.html#org.apache.spark.mllib.feature.ChiSqSelector)
method in
+`ChiSqSelector` which can take an input of `RDD[LabeledPoint]` with
categorical features, learn the summary statistics, and then
+return a model which can transform the input dataset into the reduced
feature space.
+
+This model implements
[`VectorTransformer`](api/scala/index.html#org.apache.spark.mllib.feature.VectorTransformer)
+which can apply the Chi-Squared feature selection on a `Vector` to produce
a reduced `Vector` or on
+an `RDD[Vector]` to produce a reduced `RDD[Vector]`.
+
+Note that the model that performs actual feature filtering can be
instantiated independently with array of feature indices that has to be sorted
ascending.
+</div>
--- End diff --
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