package regression
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Type Members
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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.
:: 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.
- Annotations
- @Experimental() @Since( "1.6.0" )
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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" )
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class
DecisionTreeRegressionModel extends PredictionModel[Vector, DecisionTreeRegressionModel] with DecisionTreeModel with DecisionTreeRegressorParams with MLWritable with Serializable
Decision tree (Wikipedia) model for regression.
Decision tree (Wikipedia) model for regression. It supports both continuous and categorical features.
- Annotations
- @Since( "1.4.0" )
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class
DecisionTreeRegressor extends Predictor[Vector, DecisionTreeRegressor, DecisionTreeRegressionModel] with DecisionTreeRegressorParams with DefaultParamsWritable
Decision tree learning algorithm for regression.
Decision tree learning algorithm for regression. It supports both continuous and categorical features.
- Annotations
- @Since( "1.4.0" )
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class
GBTRegressionModel extends PredictionModel[Vector, GBTRegressionModel] with GBTRegressorParams with TreeEnsembleModel[DecisionTreeRegressionModel] with MLWritable with Serializable
Gradient-Boosted Trees (GBTs) model for regression.
Gradient-Boosted Trees (GBTs) model for regression. It supports both continuous and categorical features.
- Annotations
- @Since( "1.4.0" )
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class
GBTRegressor extends Predictor[Vector, GBTRegressor, GBTRegressionModel] with GBTRegressorParams with DefaultParamsWritable with Logging
Gradient-Boosted Trees (GBTs) learning algorithm for regression.
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" )
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class
GeneralizedLinearRegression extends Regressor[Vector, GeneralizedLinearRegression, GeneralizedLinearRegressionModel] with GeneralizedLinearRegressionBase with DefaultParamsWritable with Logging
:: Experimental ::
:: Experimental ::
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). It supports "gaussian", "binomial", "poisson", "gamma" and "tweedie" 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"
- "tweedie" : power link function specified through "linkPower". The default link power in the tweedie family is 1 - variancePower.
- Annotations
- @Experimental() @Since( "2.0.0" )
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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" )
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class
GeneralizedLinearRegressionSummary extends Serializable
:: Experimental :: Summary of GeneralizedLinearRegression model and predictions.
:: Experimental :: Summary of GeneralizedLinearRegression model and predictions.
- Annotations
- @Since( "2.0.0" ) @Experimental()
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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()
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class
IsotonicRegression extends Estimator[IsotonicRegressionModel] with IsotonicRegressionBase with DefaultParamsWritable
Isotonic regression.
Isotonic regression.
Currently implemented using parallelized pool adjacent violators algorithm. Only univariate (single feature) algorithm supported.
- Annotations
- @Since( "1.5.0" )
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class
IsotonicRegressionModel extends Model[IsotonicRegressionModel] with IsotonicRegressionBase with MLWritable
Model fitted by IsotonicRegression.
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" )
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class
LinearRegression extends Regressor[Vector, LinearRegression, LinearRegressionModel] with LinearRegressionParams with DefaultParamsWritable with Logging
Linear regression.
Linear regression.
The learning objective is to minimize the specified loss function, with regularization. This supports two kinds of loss:
- squaredError (a.k.a squared loss)
- huber (a hybrid of squared error for relatively small errors and absolute error for relatively large ones, and we estimate the scale parameter from training data)
This supports multiple types of regularization:
- none (a.k.a. ordinary least squares)
- L2 (ridge regression)
- L1 (Lasso)
- L2 + L1 (elastic net)
The squared error objective function is:
$$ \begin{align} \min_{w}\frac{1}{2n}{\sum_{i=1}^n(X_{i}w - y_{i})^{2} + \lambda\left[\frac{1-\alpha}{2}{||w||_{2}}^{2} + \alpha{||w||_{1}}\right]} \end{align} $$
The huber objective function is:
$$ \begin{align} \min_{w, \sigma}\frac{1}{2n}{\sum_{i=1}^n\left(\sigma + H_m\left(\frac{X_{i}w - y_{i}}{\sigma}\right)\sigma\right) + \frac{1}{2}\lambda {||w||_2}^2} \end{align} $$
where
$$ \begin{align} H_m(z) = \begin{cases} z^2, & \text {if } |z| < \epsilon, \\ 2\epsilon|z| - \epsilon^2, & \text{otherwise} \end{cases} \end{align} $$
Note: Fitting with huber loss only supports none and L2 regularization.
- Annotations
- @Since( "1.3.0" )
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class
LinearRegressionModel extends RegressionModel[Vector, LinearRegressionModel] with LinearRegressionParams with GeneralMLWritable
Model produced by LinearRegression.
Model produced by LinearRegression.
- Annotations
- @Since( "1.3.0" )
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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()
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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()
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class
RandomForestRegressionModel extends PredictionModel[Vector, RandomForestRegressionModel] with RandomForestRegressorParams with TreeEnsembleModel[DecisionTreeRegressionModel] with MLWritable with Serializable
Random Forest model for regression.
Random Forest model for regression. It supports both continuous and categorical features.
- Annotations
- @Since( "1.4.0" )
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class
RandomForestRegressor extends Predictor[Vector, RandomForestRegressor, RandomForestRegressionModel] with RandomForestRegressorParams with DefaultParamsWritable
Random Forest learning algorithm for regression.
Random Forest learning algorithm for regression. It supports both continuous and categorical features.
- Annotations
- @Since( "1.4.0" )
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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()
Value Members
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object
AFTSurvivalRegression extends DefaultParamsReadable[AFTSurvivalRegression] with Serializable
- Annotations
- @Since( "1.6.0" )
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object
AFTSurvivalRegressionModel extends MLReadable[AFTSurvivalRegressionModel] with Serializable
- Annotations
- @Since( "1.6.0" )
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object
DecisionTreeRegressionModel extends MLReadable[DecisionTreeRegressionModel] with Serializable
- Annotations
- @Since( "2.0.0" )
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object
DecisionTreeRegressor extends DefaultParamsReadable[DecisionTreeRegressor] with Serializable
- Annotations
- @Since( "1.4.0" )
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object
GBTRegressionModel extends MLReadable[GBTRegressionModel] with Serializable
- Annotations
- @Since( "2.0.0" )
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object
GBTRegressor extends DefaultParamsReadable[GBTRegressor] with Serializable
- Annotations
- @Since( "1.4.0" )
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object
GeneralizedLinearRegression extends DefaultParamsReadable[GeneralizedLinearRegression] with Serializable
- Annotations
- @Since( "2.0.0" )
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object
GeneralizedLinearRegressionModel extends MLReadable[GeneralizedLinearRegressionModel] with Serializable
- Annotations
- @Since( "2.0.0" )
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object
IsotonicRegression extends DefaultParamsReadable[IsotonicRegression] with Serializable
- Annotations
- @Since( "1.6.0" )
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object
IsotonicRegressionModel extends MLReadable[IsotonicRegressionModel] with Serializable
- Annotations
- @Since( "1.6.0" )
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object
LinearRegression extends DefaultParamsReadable[LinearRegression] with Serializable
- Annotations
- @Since( "1.6.0" )
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object
LinearRegressionModel extends MLReadable[LinearRegressionModel] with Serializable
- Annotations
- @Since( "1.6.0" )
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object
RandomForestRegressionModel extends MLReadable[RandomForestRegressionModel] with Serializable
- Annotations
- @Since( "2.0.0" )
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object
RandomForestRegressor extends DefaultParamsReadable[RandomForestRegressor] with Serializable
- Annotations
- @Since( "1.4.0" )