Package org.apache.spark.ml.regression

package org.apache.spark.ml.regression

Related Packages

Package	Description
org.apache.spark.ml	DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

Classes

Class	Description
AFTSurvivalRegression	Fit a parametric survival regression model named accelerated failure time (AFT) model (see Accelerated failure time model (Wikipedia)) based on the Weibull distribution of the survival time.
AFTSurvivalRegressionModel	Model produced by AFTSurvivalRegression.
AFTSurvivalRegressionModel.Data$
DecisionTreeRegressionModel	Decision tree (Wikipedia) model for regression.
DecisionTreeRegressor	Decision tree learning algorithm for regression.
FMRegressionModel	Model produced by FMRegressor.
FMRegressionModel.Data$
FMRegressor	Factorization Machines learning algorithm for regression.
GBTRegressionModel	Gradient-Boosted Trees (GBTs) model for regression.
GBTRegressor	Gradient-Boosted Trees (GBTs) learning algorithm for regression.
GeneralizedLinearRegression	Fit a Generalized Linear Model (see Generalized linear model (Wikipedia)) specified by giving a symbolic description of the linear predictor (link function) and a description of the error distribution (family).
GeneralizedLinearRegression.Binomial$	Binomial exponential family distribution.
GeneralizedLinearRegression.CLogLog$
GeneralizedLinearRegression.Family$
GeneralizedLinearRegression.FamilyAndLink$
GeneralizedLinearRegression.Gamma$	Gamma exponential family distribution.
GeneralizedLinearRegression.Gaussian$	Gaussian exponential family distribution.
GeneralizedLinearRegression.Identity$
GeneralizedLinearRegression.Inverse$
GeneralizedLinearRegression.Link$
GeneralizedLinearRegression.Log$
GeneralizedLinearRegression.Logit$
GeneralizedLinearRegression.Poisson$	Poisson exponential family distribution.
GeneralizedLinearRegression.Probit$
GeneralizedLinearRegression.Sqrt$
GeneralizedLinearRegression.Tweedie$
GeneralizedLinearRegressionModel	Model produced by GeneralizedLinearRegression.
GeneralizedLinearRegressionModel.Data$
GeneralizedLinearRegressionSummary	Summary of GeneralizedLinearRegression model and predictions.
GeneralizedLinearRegressionTrainingSummary	Summary of GeneralizedLinearRegression fitting and model.
IsotonicRegression	Isotonic regression.
IsotonicRegressionModel	Model fitted by IsotonicRegression.
IsotonicRegressionModel.Data$
LinearRegression	Linear regression.
LinearRegressionModel	Model produced by LinearRegression.
LinearRegressionSummary	Linear regression results evaluated on a dataset.
LinearRegressionTrainingSummary	Linear regression training results.
RandomForestRegressionModel	Random Forest model for regression.
RandomForestRegressor	Random Forest learning algorithm for regression.
RegressionModel<FeaturesType,M extends RegressionModel<FeaturesType,M>>	Model produced by a Regressor.
Regressor<FeaturesType,Learner extends Regressor<FeaturesType,Learner,M>,M extends RegressionModel<FeaturesType,M>>	Single-label regression