Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/15212#discussion_r93725408
--- Diff: docs/mllib-feature-extraction.md ---
@@ -227,11 +227,13 @@ both speed and statistical learning behavior.
[`ChiSqSelector`](api/scala/index.html#org.apache.spark.mllib.feature.ChiSqSelector)
implements
Chi-Squared feature selection. It operates on labeled data with
categorical features. ChiSqSelector uses the
[Chi-Squared test of
independence](https://en.wikipedia.org/wiki/Chi-squared_test) to decide which
-features to choose. It supports three selection methods: `numTopFeatures`,
`percentile`, `fpr`:
+features to choose. It supports five selection methods: `numTopFeatures`,
`percentile`, `fpr`, `fdr`, `fwe`:
* `numTopFeatures` chooses a fixed number of top features according to a
chi-squared test. This is akin to yielding the features with the most
predictive power.
* `percentile` is similar to `numTopFeatures` but chooses a fraction of
all features instead of a fixed number.
* `fpr` chooses all features whose p-value is below a threshold, thus
controlling the false positive rate of selection.
+* `fdr` chooses all features whose false discovery rate meets some
threshold.
+* `fwe` chooses all features whose family-wise error rate meets some
threshold.
--- End diff --
Update according the above suggestion.
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