 # IsotonicRegression

### Related Doc: package regression

#### 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: Grotzinger, S. J., and C. Witzgall. "Projections onto order simplexes." Applied mathematics and Optimization 12.1 (1984): 247-270.

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

Isotonic regression (Wikipedia)

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### Instance Constructors

1. #### new IsotonicRegression()

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

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

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.

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@Since( "1.3.0" )
17. #### def setIsotonic(isotonic: Boolean): IsotonicRegression.this.type

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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