| Interface | Description |
|---|---|
| AFTSurvivalRegressionParams |
Params for accelerated failure time (AFT) regression.
|
| FactorizationMachines | |
| FactorizationMachinesParams |
Params for Factorization Machines
|
| FMRegressorParams |
Params for FMRegressor
|
| GeneralizedLinearRegressionBase |
Params for Generalized Linear Regression.
|
| IsotonicRegressionBase |
Params for isotonic regression.
|
| LinearRegressionParams |
Params for linear regression.
|
| 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 |
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.
|
| AFTSurvivalRegressionModel |
Model produced by
AFTSurvivalRegression. |
| DecisionTreeRegressionModel |
Decision tree (Wikipedia) model for regression.
|
| DecisionTreeRegressor |
Decision tree
learning algorithm for regression.
|
| FMRegressionModel |
Model produced by
FMRegressor. |
| FMRegressor |
Factorization Machines learning algorithm for regression.
|
| GBTRegressionModel |
Gradient-Boosted Trees (GBTs)
model for regression.
|
| GBTRegressor |
Gradient-Boosted Trees (GBTs)
learning algorithm for regression.
|
| GeneralizedLinearRegression |
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).
|
| GeneralizedLinearRegression.Binomial$ |
Binomial exponential family distribution.
|
| GeneralizedLinearRegression.CLogLog$ | |
| GeneralizedLinearRegression.Family$ | |
| GeneralizedLinearRegression.FamilyAndLink$ | |
| 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$ | |
| GeneralizedLinearRegression.Tweedie$ | |
| GeneralizedLinearRegressionModel |
Model produced by
GeneralizedLinearRegression. |
| GeneralizedLinearRegressionSummary |
Summary of
GeneralizedLinearRegression model and predictions. |
| GeneralizedLinearRegressionTrainingSummary |
Summary of
GeneralizedLinearRegression fitting and model. |
| InternalLinearRegressionModelWriter |
A writer for LinearRegression that handles the "internal" (or default) format
|
| IsotonicRegression |
Isotonic regression.
|
| IsotonicRegressionModel |
Model fitted by IsotonicRegression.
|
| LinearRegression |
Linear regression.
|
| LinearRegressionModel |
Model produced by
LinearRegression. |
| LinearRegressionSummary |
Linear regression results evaluated on a dataset.
|
| LinearRegressionTrainingSummary |
Linear regression training results.
|
| PMMLLinearRegressionModelWriter |
A writer for LinearRegression that handles the "pmml" format
|
| RandomForestRegressionModel |
Random Forest model for regression.
|
| RandomForestRegressor |
Random Forest
learning algorithm for regression.
|
| RegressionModel<FeaturesType,M extends RegressionModel<FeaturesType,M>> |
Model produced by a
Regressor. |
| Regressor<FeaturesType,Learner extends Regressor<FeaturesType,Learner,M>,M extends RegressionModel<FeaturesType,M>> |
Single-label regression
|