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From yanboliang <...@git.apache.org>
Subject [GitHub] spark pull request #15212: [SPARK-17645][MLLIB][ML]add feature selector meth...
Date Fri, 23 Dec 2016 14:00:36 GMT
Github user yanboliang commented on a diff in the pull request:

    https://github.com/apache/spark/pull/15212#discussion_r93766100
  
    --- 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` 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.
    +* `fwe` chooses all features whose whose p-values is below a threshold, thus controlling
the family-wise error rate of selection.
    --- End diff --
    
    Remove duplicated ```whose```.


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