Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/15212#discussion_r93725098
--- Diff: docs/ml-features.md ---
@@ -1423,12 +1423,12 @@ for more details on the API.
`ChiSqSelector` stands for 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.
--- End diff --
``` `fdr` uses the [Benjamini-Hochberg
procedure](https://en.wikipedia.org/wiki/False_discovery_rate#Benjamini.E2.80.93Hochberg_procedure)
to choose all features whose false discovery rate is below a threshold```
should be better?
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