IsotonicRegression¶
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class
pyspark.mllib.regression.IsotonicRegression[source]¶ Isotonic regression. Currently implemented using parallelized pool adjacent violators algorithm. Only univariate (single feature) algorithm supported.
New in version 1.4.0.
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
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classmethod
train(data: pyspark.rdd.RDD[VectorLike], isotonic: bool = True) → pyspark.mllib.regression.IsotonicRegressionModel[source]¶ Train an isotonic regression model on the given data.
New in version 1.4.0.
- Parameters
- data
pyspark.RDD RDD of (label, feature, weight) tuples.
- isotonicbool, optional
Whether this is isotonic (which is default) or antitonic. (default: True)
- data