IsotonicRegression

class pyspark.mllib.regression.IsotonicRegression

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

Notes

Sequential PAV implementation based on Tibshirani, Ryan J., Holger Hoefling, and Robert Tibshirani (2011) [1]

Sequential PAV parallelization based on Kearsley, Anthony J., Richard A. Tapia, and Michael W. Trosset (1996) [2]

See also Isotonic regression (Wikipedia).

1

Tibshirani, Ryan J., Holger Hoefling, and Robert Tibshirani. “Nearly-isotonic regression.” Technometrics 53.1 (2011): 54-61. Available from http://www.stat.cmu.edu/~ryantibs/papers/neariso.pdf

2

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 http://softlib.rice.edu/pub/CRPC-TRs/reports/CRPC-TR96640.pdf

Methods

train(data[, isotonic])

Train an isotonic regression model on the given data.

Methods Documentation

classmethod train(data: pyspark.rdd.RDD[VectorLike], isotonic: bool = True)pyspark.mllib.regression.IsotonicRegressionModel

Train an isotonic regression model on the given data.

Parameters
datapyspark.RDD

RDD of (label, feature, weight) tuples.

isotonicbool, optional

Whether this is isotonic (which is default) or antitonic. (default: True)