| Class | Description |
|---|---|
| AFTAggregator |
AFTAggregator computes the gradient and loss for a AFT loss function,
as used in AFT survival regression for samples in sparse or dense vector in an online fashion.
|
| AFTCostFun |
AFTCostFun implements Breeze's DiffFunction[T] for AFT cost.
|
| AFTSurvivalRegression |
:: Experimental ::
Fit a parametric survival regression model named accelerated failure time (AFT) model
(
https://en.wikipedia.org/wiki/Accelerated_failure_time_model)
based on the Weibull distribution of the survival time. |
| AFTSurvivalRegressionModel |
:: Experimental ::
Model produced by
AFTSurvivalRegression. |
| DecisionTreeRegressionModel |
Decision tree model for regression. |
| DecisionTreeRegressor |
Decision tree learning algorithm
for regression. |
| GBTRegressionModel |
Gradient-Boosted Trees (GBTs)
model for regression. |
| GBTRegressor |
Gradient-Boosted Trees (GBTs)
learning algorithm for regression. |
| GeneralizedLinearRegression |
:: Experimental ::
|
| GeneralizedLinearRegression.Binomial$ |
Binomial exponential family distribution.
|
| GeneralizedLinearRegression.CLogLog$ | |
| GeneralizedLinearRegression.Family$ | |
| GeneralizedLinearRegression.Gamma$ |
Gamma exponential family distribution.
|
| GeneralizedLinearRegression.Gaussian$ |
Gaussian exponential family distribution.
|
| GeneralizedLinearRegression.Identity$ | |
| GeneralizedLinearRegression.Inverse$ | |
| GeneralizedLinearRegression.Link$ | |
| GeneralizedLinearRegression.Log$ | |
| GeneralizedLinearRegression.Logit$ | |
| GeneralizedLinearRegression.Poisson$ |
Poisson exponential family distribution.
|
| GeneralizedLinearRegression.Probit$ | |
| GeneralizedLinearRegression.Sqrt$ | |
| GeneralizedLinearRegressionModel |
:: Experimental ::
Model produced by
GeneralizedLinearRegression. |
| GeneralizedLinearRegressionSummary |
:: Experimental ::
Summary of
GeneralizedLinearRegression model and predictions. |
| GeneralizedLinearRegressionTrainingSummary |
:: Experimental ::
Summary of
GeneralizedLinearRegression fitting and model. |
| IsotonicRegression |
Isotonic regression.
|
| IsotonicRegressionModel |
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 an online fashion.
|
| LeastSquaresCostFun |
LeastSquaresCostFun implements Breeze's DiffFunction[T] for Least Squares cost.
|
| LinearRegression |
Linear regression.
|
| LinearRegressionModel |
Model produced by
LinearRegression. |
| LinearRegressionSummary |
:: Experimental ::
Linear regression results evaluated on a dataset.
|
| LinearRegressionTrainingSummary |
:: Experimental ::
Linear regression training results.
|
| RandomForestRegressionModel |
Random Forest model for regression. |
| RandomForestRegressor |
Random Forest learning algorithm for regression. |
| RegressionModel<FeaturesType,M extends RegressionModel<FeaturesType,M>> |
:: DeveloperApi ::
|