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| Class Summary | |
|---|---|
| DecisionTreeRegressionModel | :: Experimental ::
 Decision tree model for regression. | 
| DecisionTreeRegressor | :: Experimental ::
 Decision tree learning algorithm
 for regression. | 
| GBTRegressionModel | :: Experimental :: | 
| GBTRegressor | :: Experimental ::
 Gradient-Boosted Trees (GBTs)
 learning algorithm for regression. | 
| LeastSquaresAggregator | LeastSquaresAggregator computes the gradient and loss for a Least-squared loss function, as used in linear regression for samples in sparse or dense vector in a online fashion. | 
| LeastSquaresCostFun | LeastSquaresCostFun implements Breeze's DiffFunction[T] for Least Squares cost. | 
| LinearRegression | :: Experimental :: Linear regression. | 
| LinearRegressionModel | :: Experimental ::
 Model produced by LinearRegression. | 
| RandomForestRegressionModel | :: Experimental ::
 Random Forest model for regression. | 
| RandomForestRegressor | :: Experimental ::
 Random Forest learning algorithm for regression. | 
| RegressionModel<FeaturesType,M extends RegressionModel<FeaturesType,M>> | :: DeveloperApi :: | 
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