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Showing content from http://spark.apache.org/docs/latest/api/java/org/apache/spark/mllib/feature/ChiSqSelector.html below:

ChiSqSelector (Spark 4.0.0 JavaDoc)

org.apache.spark.mllib.feature.ChiSqSelector

All Implemented Interfaces:

Serializable

Creates a ChiSquared feature selector. The selector supports different selection methods: numTopFeatures, percentile, fpr, fdr, fwe. - numTopFeatures chooses a fixed number of top features according to a chi-squared test. - percentile is similar but chooses a fraction of all features instead of a fixed number. - fpr chooses all features whose p-values are 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 p-values are below a threshold. The threshold is scaled by 1/numFeatures, thus controlling the family-wise error rate of selection. By default, the selection method is numTopFeatures, with the default number of top features set to 50.

See Also:

• Serialized Form

• Constructor Summary

  Constructors

  The is the same to call this() and setNumTopFeatures(numTopFeatures)

• Method Summary

  double

  Returns a ChiSquared feature selector.

  double

  double

  int

  double

  Set of selector types that ChiSqSelector supports.

• Constructor Details
  • ChiSqSelector

    public ChiSqSelector()

  • ChiSqSelector

    public ChiSqSelector(int numTopFeatures)

    The is the same to call this() and setNumTopFeatures(numTopFeatures)

    Parameters:

    numTopFeatures - (undocumented)

• Method Details
  • supportedSelectorTypes public static String[] supportedSelectorTypes

    ()

    Set of selector types that ChiSqSelector supports.

  • numTopFeatures

    public int numTopFeatures()

  • percentile

    public double percentile()

  • fpr

    public double fpr()

  • fdr

    public double fdr()

  • fwe

    public double fwe()

  • selectorType
  • setNumTopFeatures
  • setPercentile
  • setFpr
  • setFdr
  • setFwe
  • setSelectorType
  • fit

    Parameters:

    data - an RDD[LabeledPoint] containing the labeled dataset with categorical features. Real-valued features will be treated as categorical for each distinct value. Apply feature discretizer before using this function.

    Returns:

    (undocumented)

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