regression

Type Members

1. final class DecisionTreeRegressionModel extends PredictionModel[Vector, DecisionTreeRegressionModel] with DecisionTreeModel with Serializable

   

   :: Experimental :: Decision tree model for regression.

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

   Annotations

   @Experimental()

2. final class DecisionTreeRegressor extends Predictor[Vector, DecisionTreeRegressor, DecisionTreeRegressionModel] with DecisionTreeParams with TreeRegressorParams

   

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

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

   Annotations

   @Experimental()

3. final class GBTRegressionModel extends PredictionModel[Vector, GBTRegressionModel] with TreeEnsembleModel with Serializable

   

   :: Experimental ::

   :: Experimental ::

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

   Annotations

   @Experimental()

4. final class GBTRegressor extends Predictor[Vector, GBTRegressor, GBTRegressionModel] with GBTParams with TreeRegressorParams 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.

   Annotations

   @Experimental()

5. class IsotonicRegression extends Estimator[IsotonicRegressionModel] with IsotonicRegressionBase

   

   :: 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

   @Experimental()

6. class IsotonicRegressionModel extends Model[IsotonicRegressionModel] with IsotonicRegressionBase

   

   :: 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

   @Experimental()

7. class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegressionModel] with LinearRegressionParams 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 weights - y||²

   This support multiple types of regularization:

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

   Annotations

   @Experimental()

8. class LinearRegressionModel extends RegressionModel[Vector, LinearRegressionModel] with LinearRegressionParams

   

   :: Experimental :: Model produced by LinearRegression.

   :: Experimental :: Model produced by LinearRegression.

   Annotations

   @Experimental()

9. class LinearRegressionSummary extends Serializable

   

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

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

   Annotations

   @Experimental()

10. class LinearRegressionTrainingSummary extends LinearRegressionSummary

    

    :: Experimental :: Linear regression training results.

    :: Experimental :: Linear regression training results.

    Annotations

    @Experimental()

11. final class RandomForestRegressionModel extends PredictionModel[Vector, RandomForestRegressionModel] with TreeEnsembleModel with Serializable

    

    :: Experimental :: Random Forest model for regression.

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

    Annotations

    @Experimental()

12. final class RandomForestRegressor extends Predictor[Vector, RandomForestRegressor, RandomForestRegressionModel] with RandomForestParams with TreeRegressorParams

    

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

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

    Annotations

    @Experimental()

13. 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()