Github user mpjlu commented on a diff in the pull request:
https://github.com/apache/spark/pull/15647#discussion_r85310898
--- Diff: docs/mllib-feature-extraction.md ---
@@ -227,22 +227,19 @@ 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: `KBest`,
`Percentile` and `FPR`:
+features to choose. It supports three selection methods: `numTopFeatures`,
`percentile`, `fpr`:
-* `KBest` chooses the `k` top features according to a chi-squared test.
This is akin to yielding the features with the most predictive power.
-* `Percentile` is similar to `KBest` but chooses a fraction of all
features instead of a fixed number.
-* `FPR` chooses all features whose false positive rate meets some
threshold.
+* `numTopFeatures` chooses the `k` 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.
-By default, the selection method is `KBest`, the default number of top
features is 50. User can use
-`setNumTopFeatures`, `setPercentile` and `setAlpha` to set different
selection methods.
+By default, the selection method is `numTopFeatures`, with the default
number of top features set to 50. User can use
+`setNumTopFeatures`, `setPercentile`, `fpr` to set different selection
methods.
--- End diff --
ditto
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