| Class | Description |
|---|---|
| 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. |
| IsotonicRegression | |
| IsotonicRegressionModel |
:: Experimental ::
Model fitted by IsotonicRegression.
|
| 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. |
| LinearRegressionSummary |
:: Experimental ::
Linear regression results evaluated on a dataset.
|
| LinearRegressionTrainingSummary |
:: Experimental ::
Linear regression training results.
|
| RandomForestRegressionModel |
:: Experimental ::
Random Forest model for regression. |
| RandomForestRegressor |
:: Experimental ::
Random Forest learning algorithm for regression. |
| RegressionModel<FeaturesType,M extends RegressionModel<FeaturesType,M>> |
:: DeveloperApi ::
|