:: 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.
:: Experimental :: Model produced by AFTSurvivalRegression.
:: Experimental :: Model produced by AFTSurvivalRegression.
:: Experimental :: Decision tree model for regression.
:: Experimental :: Decision tree model for regression. It supports both continuous and categorical features.
:: Experimental :: Decision tree learning algorithm for regression.
:: Experimental :: Decision tree learning algorithm for regression. It supports both continuous and categorical features.
:: Experimental ::
:: Experimental ::
Gradient-Boosted Trees (GBTs) model for regression. It supports both continuous and categorical features.
:: Experimental :: Gradient-Boosted Trees (GBTs) learning algorithm for regression.
:: Experimental :: Gradient-Boosted Trees (GBTs) learning algorithm for regression. It supports both continuous and categorical features.
:: Experimental :: Isotonic regression.
:: Experimental :: Isotonic regression.
Currently implemented using parallelized pool adjacent violators algorithm. Only univariate (single feature) algorithm supported.
:: Experimental :: Model fitted by IsotonicRegression.
:: Experimental :: Model fitted by IsotonicRegression. Predicts using a piecewise linear function.
For detailed rules see org.apache.spark.mllib.regression.IsotonicRegressionModel.predict().
:: Experimental :: Linear regression.
:: Experimental :: Linear regression.
The learning objective is to minimize the squared error, with regularization. The specific squared error loss function used is: L = 1/2n ||A coefficients - y||2
This support multiple types of regularization:
:: Experimental :: Model produced by LinearRegression.
:: Experimental :: Model produced by LinearRegression.
:: Experimental :: Linear regression results evaluated on a dataset.
:: Experimental :: Linear regression results evaluated on a dataset.
:: Experimental :: Linear regression training results.
:: Experimental :: Linear regression training results. Currently, the training summary ignores the training coefficients except for the objective trace.
:: Experimental :: Random Forest model for regression.
:: Experimental :: Random Forest model for regression. It supports both continuous and categorical features.
:: Experimental :: Random Forest learning algorithm for regression.
:: Experimental :: Random Forest learning algorithm for regression. It supports both continuous and categorical features.
:: DeveloperApi ::
:: DeveloperApi ::
Model produced by a Regressor.
Type of input features. E.g., org.apache.spark.mllib.linalg.Vector
Concrete Model type.
:: 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.