Class

org.apache.spark.mllib.regression

IsotonicRegression

Related Doc: package regression

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class IsotonicRegression extends Serializable

Isotonic regression. Currently implemented using parallelized pool adjacent violators algorithm. Only univariate (single feature) algorithm supported.

Sequential PAV implementation based on: Tibshirani, Ryan J., Holger Hoefling, and Robert Tibshirani. "Nearly-isotonic regression." Technometrics 53.1 (2011): 54-61. Available from here

Sequential PAV parallelization based on: Kearsley, Anthony J., Richard A. Tapia, and Michael W. Trosset. "An approach to parallelizing isotonic regression." Applied Mathematics and Parallel Computing. Physica-Verlag HD, 1996. 141-147. Available from here

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@Since( "1.3.0" )
Source
IsotonicRegression.scala
See also

Isotonic regression (Wikipedia)

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  1. new IsotonicRegression()

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    Constructs IsotonicRegression instance with default parameter isotonic = true.

    Constructs IsotonicRegression instance with default parameter isotonic = true.

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    @Since( "1.3.0" )

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  15. def run(input: JavaRDD[(Double, Double, Double)]): IsotonicRegressionModel

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    Run pool adjacent violators algorithm to obtain isotonic regression model.

    Run pool adjacent violators algorithm to obtain isotonic regression model.

    input

    JavaRDD of tuples (label, feature, weight) where label is dependent variable for which we calculate isotonic regression, feature is independent variable and weight represents number of measures with default 1. If multiple labels share the same feature value then they are ordered before the algorithm is executed.

    returns

    Isotonic regression model.

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    @Since( "1.3.0" )

  16. def run(input: RDD[(Double, Double, Double)]): IsotonicRegressionModel

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    Run IsotonicRegression algorithm to obtain isotonic regression model.

    Run IsotonicRegression algorithm to obtain isotonic regression model.

    input

    RDD of tuples (label, feature, weight) where label is dependent variable for which we calculate isotonic regression, feature is independent variable and weight represents number of measures with default 1. If multiple labels share the same feature value then they are ordered before the algorithm is executed.

    returns

    Isotonic regression model.

    Annotations
    @Since( "1.3.0" )

  17. def setIsotonic(isotonic: Boolean): IsotonicRegression.this.type

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    Sets the isotonic parameter.

    Sets the isotonic parameter.

    isotonic

    Isotonic (increasing) or antitonic (decreasing) sequence.

    returns

    This instance of IsotonicRegression.

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    @Since( "1.3.0" )

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