regression

Type Members

1. class AFTSurvivalRegression extends Estimator[AFTSurvivalRegressionModel] with AFTSurvivalRegressionParams with DefaultParamsWritable with Logging

   

   :: Experimental :: Fit a parametric survival regression model named accelerated failure time (AFT) model (https://en.wikipedia.org/wiki/Accelerated_failure_time_model) based on the Weibull distribution of the survival time.

   :: Experimental :: Fit a parametric survival regression model named accelerated failure time (AFT) model (https://en.wikipedia.org/wiki/Accelerated_failure_time_model) based on the Weibull distribution of the survival time.

   Annotations

   @Experimental() @Since( "1.6.0" )

2. class AFTSurvivalRegressionModel extends Model[AFTSurvivalRegressionModel] with AFTSurvivalRegressionParams with MLWritable

   

   :: Experimental :: Model produced by AFTSurvivalRegression.

   :: Experimental :: Model produced by AFTSurvivalRegression.

   Annotations

   @Experimental() @Since( "1.6.0" )

3. class DecisionTreeRegressionModel extends PredictionModel[Vector, DecisionTreeRegressionModel] with DecisionTreeModel with DecisionTreeRegressorParams with MLWritable with Serializable

   

   :: Experimental :: Decision tree model for regression.

   :: Experimental :: Decision tree model for regression. It supports both continuous and categorical features.

   Annotations

   @Since( "1.4.0" ) @Experimental()

4. class DecisionTreeRegressor extends Predictor[Vector, DecisionTreeRegressor, DecisionTreeRegressionModel] with DecisionTreeRegressorParams with DefaultParamsWritable

   

   :: Experimental :: Decision tree learning algorithm for regression.

   :: Experimental :: Decision tree learning algorithm for regression. It supports both continuous and categorical features.

   Annotations

   @Since( "1.4.0" ) @Experimental()

5. class GBTRegressionModel extends PredictionModel[Vector, GBTRegressionModel] with GBTRegressorParams with TreeEnsembleModel[DecisionTreeRegressionModel] with MLWritable with Serializable

   

   :: Experimental ::

   :: Experimental ::

   Gradient-Boosted Trees (GBTs) model for regression. It supports both continuous and categorical features.

   Annotations

   @Since( "1.4.0" ) @Experimental()

6. class GBTRegressor extends Predictor[Vector, GBTRegressor, GBTRegressionModel] with GBTRegressorParams with DefaultParamsWritable with Logging

   

   :: Experimental :: Gradient-Boosted Trees (GBTs) learning algorithm for regression.

   :: Experimental :: Gradient-Boosted Trees (GBTs) learning algorithm for regression. It supports both continuous and categorical features.

   The implementation is based upon: J.H. Friedman. "Stochastic Gradient Boosting." 1999.

   Notes on Gradient Boosting vs. TreeBoost:

   • This implementation is for Stochastic Gradient Boosting, not for TreeBoost.
   • Both algorithms learn tree ensembles by minimizing loss functions.
   • TreeBoost (Friedman, 1999) additionally modifies the outputs at tree leaf nodes based on the loss function, whereas the original gradient boosting method does not.
     • When the loss is SquaredError, these methods give the same result, but they could differ for other loss functions.
   • We expect to implement TreeBoost in the future: [https://issues.apache.org/jira/browse/SPARK-4240]

   Annotations

   @Since( "1.4.0" ) @Experimental()

7. class GeneralizedLinearRegression extends Regressor[Vector, GeneralizedLinearRegression, GeneralizedLinearRegressionModel] with GeneralizedLinearRegressionBase with DefaultParamsWritable with Logging

   

   :: Experimental ::

   :: Experimental ::

   Fit a Generalized Linear Model (https://en.wikipedia.org/wiki/Generalized_linear_model) specified by giving a symbolic description of the linear predictor (link function) and a description of the error distribution (family). It supports "gaussian", "binomial", "poisson" and "gamma" as family. Valid link functions for each family is listed below. The first link function of each family is the default one.

   • "gaussian" -> "identity", "log", "inverse"
   • "binomial" -> "logit", "probit", "cloglog"
   • "poisson" -> "log", "identity", "sqrt"
   • "gamma" -> "inverse", "identity", "log"

   Annotations

   @Experimental() @Since( "2.0.0" )

8. class GeneralizedLinearRegressionModel extends RegressionModel[Vector, GeneralizedLinearRegressionModel] with GeneralizedLinearRegressionBase with MLWritable

   

   :: Experimental :: Model produced by GeneralizedLinearRegression.

   :: Experimental :: Model produced by GeneralizedLinearRegression.

   Annotations

   @Experimental() @Since( "2.0.0" )

9. class GeneralizedLinearRegressionSummary extends Serializable

   

   :: Experimental :: Summary of GeneralizedLinearRegression model and predictions.

   :: Experimental :: Summary of GeneralizedLinearRegression model and predictions.

   Annotations

   @Since( "2.0.0" ) @Experimental()

10. class GeneralizedLinearRegressionTrainingSummary extends GeneralizedLinearRegressionSummary with Serializable

    

    :: Experimental :: Summary of GeneralizedLinearRegression fitting and model.

    :: Experimental :: Summary of GeneralizedLinearRegression fitting and model.

    Annotations

    @Since( "2.0.0" ) @Experimental()

11. class IsotonicRegression extends Estimator[IsotonicRegressionModel] with IsotonicRegressionBase with DefaultParamsWritable

    

    :: Experimental :: Isotonic regression.

    :: Experimental :: Isotonic regression.

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

    Uses org.apache.spark.mllib.regression.IsotonicRegression.

    Annotations

    @Since( "1.5.0" ) @Experimental()

12. class IsotonicRegressionModel extends Model[IsotonicRegressionModel] with IsotonicRegressionBase with MLWritable

    

    :: Experimental :: Model fitted by IsotonicRegression.

    :: Experimental :: Model fitted by IsotonicRegression. Predicts using a piecewise linear function.

    For detailed rules see org.apache.spark.mllib.regression.IsotonicRegressionModel.predict().

    Annotations

    @Since( "1.5.0" ) @Experimental()

13. class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegressionModel] with LinearRegressionParams with DefaultParamsWritable with Logging

    

    :: Experimental :: Linear regression.

    :: Experimental :: Linear regression.

    The learning objective is to minimize the squared error, with regularization. The specific squared error loss function used is: L = 1/2n ||A coefficients - y||²

    This supports multiple types of regularization:

    • none (a.k.a. ordinary least squares)
    • L2 (ridge regression)
    • L1 (Lasso)
    • L2 + L1 (elastic net)

    Annotations

    @Since( "1.3.0" ) @Experimental()

14. class LinearRegressionModel extends RegressionModel[Vector, LinearRegressionModel] with LinearRegressionParams with MLWritable

    

    :: Experimental :: Model produced by LinearRegression.

    :: Experimental :: Model produced by LinearRegression.

    Annotations

    @Since( "1.3.0" ) @Experimental()

15. class LinearRegressionSummary extends Serializable

    

    :: Experimental :: Linear regression results evaluated on a dataset.

    :: Experimental :: Linear regression results evaluated on a dataset.

    Annotations

    @Since( "1.5.0" ) @Experimental()

16. class LinearRegressionTrainingSummary extends LinearRegressionSummary

    

    :: Experimental :: Linear regression training results.

    :: Experimental :: Linear regression training results. Currently, the training summary ignores the training weights except for the objective trace.

    Annotations

    @Since( "1.5.0" ) @Experimental()

17. class RandomForestRegressionModel extends PredictionModel[Vector, RandomForestRegressionModel] with RandomForestRegressionModelParams with TreeEnsembleModel[DecisionTreeRegressionModel] with MLWritable with Serializable

    

    :: Experimental :: Random Forest model for regression.

    :: Experimental :: Random Forest model for regression. It supports both continuous and categorical features.

    Annotations

    @Since( "1.4.0" ) @Experimental()

18. class RandomForestRegressor extends Predictor[Vector, RandomForestRegressor, RandomForestRegressionModel] with RandomForestRegressorParams with DefaultParamsWritable

    

    :: Experimental :: Random Forest learning algorithm for regression.

    :: Experimental :: Random Forest learning algorithm for regression. It supports both continuous and categorical features.

    Annotations

    @Since( "1.4.0" ) @Experimental()

19. abstract class RegressionModel[FeaturesType, M <: RegressionModel[FeaturesType, M]] extends PredictionModel[FeaturesType, M] with PredictorParams

    

    :: DeveloperApi ::

    :: DeveloperApi ::

    Model produced by a Regressor.

    FeaturesType

    Type of input features. E.g., org.apache.spark.mllib.linalg.Vector

    M

    Concrete Model type.

    Annotations

    @DeveloperApi()