Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/8752#discussion_r39461748
--- Diff: docs/mllib-feature-extraction.md ---
@@ -380,35 +380,37 @@ data2 = labels.zip(normalizer2.transform(features))
</div>
</div>
-## Feature selection
-[Feature selection](http://en.wikipedia.org/wiki/Feature_selection) allows
selecting the most relevant features for use in model construction. Feature
selection reduces the size of the vector space and, in turn, the complexity of
any subsequent operation with vectors. The number of features to select can be
tuned using a held-out validation set.
+## ChiSqSelector
-### ChiSqSelector
-[`ChiSqSelector`](api/scala/index.html#org.apache.spark.mllib.feature.ChiSqSelector)
stands for Chi-Squared feature selection. It operates on labeled data with
categorical features. `ChiSqSelector` orders features based on a Chi-Squared
test of independence from the class, and then filters (selects) the top
features which the class label depends on the most. This is akin to yielding
the features with the most predictive power.
+[Feature selection](http://en.wikipedia.org/wiki/Feature_selection) tries
to identify relevant features for use in model construction. It reduces the
size of the feature space, which can improve both speed and statistical
learning behavior.
--- End diff --
100cw
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