Package smile.regression

package smile.regression

Regression analysis. In statistics, regression analysis includes any techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables. Therefore, the estimation target is a function of the independent variables called the regression function. Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning.

Class	Description
DataFrameRegression	Regression trait on DataFrame.
DataFrameRegression.Trainer<M extends DataFrameRegression>	The regression trainer.
ElasticNet	Elastic Net regularization.
GaussianProcessRegression<T>	Gaussian Process for Regression.
GradientTreeBoost	Gradient boosting for regression.
KernelMachine<T>	The learning methods building on kernels.
LASSO	Lasso (least absolute shrinkage and selection operator) regression.
LinearModel	Linear model.
MLP	Fully connected multilayer perceptron neural network for regression.
OLS	Ordinary least squares.
RandomForest	Random forest for regression.
RandomForest.Model	The base model.
RBFNetwork<T>	Radial basis function network.
Regression<T>	Regression analysis includes any techniques for modeling and analyzing the relationship between a dependent variable and one or more independent variables.
Regression.Trainer<T,M extends Regression<T>>	The regression trainer.
RegressionTree	Regression tree.
RidgeRegression	Ridge Regression.
SVM	Epsilon support vector regression.