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 (see Accelerated failure time model (Wikipedia)) based on the Weibull distribution of the survival time.

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

   :: Experimental :: Model produced by AFTSurvivalRegression.

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

   Decision tree (Wikipedia) model for regression.

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

   Decision tree learning algorithm for regression.

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

   Gradient-Boosted Trees (GBTs) model for regression.

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

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

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

   :: Experimental ::

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

   :: Experimental :: Model produced by GeneralizedLinearRegression.

9. class GeneralizedLinearRegressionSummary extends Serializable

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

10. class GeneralizedLinearRegressionTrainingSummary extends GeneralizedLinearRegressionSummary with Serializable

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

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

    Isotonic regression.

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

    Model fitted by IsotonicRegression.

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

    Linear regression.

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

    Model produced by LinearRegression.

15. class LinearRegressionSummary extends Serializable

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

16. class LinearRegressionTrainingSummary extends LinearRegressionSummary

    :: Experimental :: Linear regression training results.

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

    Random Forest model for regression.

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

    Random Forest learning algorithm for regression.

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

    :: DeveloperApi ::