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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