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): 247270.
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. PhysicaVerlag HD, 1996. 141147. Available from here
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 @Since( "1.3.0" )
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 IsotonicRegression.scala
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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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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 aggregated using the weighted average before the algorithm is executed.
 returns
Isotonic regression model.
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 @Since( "1.3.0" )

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 aggregated using the weighted average before the algorithm is executed.
 returns
Isotonic regression model.
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 @Since( "1.3.0" )

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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