Package smile.regression

Regression analysis.

See: Description

Interface Summary

Interface	Description
OnlineRegression<T>	Regression model with online learning capability.
Regression<T>	Regression analysis includes any techniques for modeling and analyzing the relationship between a dependent variable and one or more independent variables.
RegressionTree.NodeOutput	An interface to calculate node output.

Class Summary

Class	Description
ElasticNet	Elastic Net regularization.
GaussianProcessRegression<T>	Gaussian Process for Regression.
GaussianProcessRegression.Trainer<T>	Trainer for Gaussian Process for Regression.
GradientTreeBoost	Gradient boosting for regression.
GradientTreeBoost.Trainer	Trainer for GradientTreeBoost regression.
LASSO	Lasso (least absolute shrinkage and selection operator) regression.
LASSO.Trainer	Trainer for LASSO regression.
NeuralNetwork	Multilayer perceptron neural network for regression.
NeuralNetwork.Trainer	Trainer for neural networks.
OLS	Ordinary least squares.
OLS.Trainer	Trainer for linear regression by ordinary least squares.
RandomForest	Random forest for regression.
RandomForest.Trainer	Trainer for random forest.
RBFNetwork<T>	Radial basis function network.
RBFNetwork.Trainer<T>	Trainer for RBF networks.
RegressionTrainer<T>	Abstract regression model trainer.
RegressionTree	Decision tree for regression.
RegressionTree.Trainer	Trainer for regression tree.
RidgeRegression	Ridge Regression.
RidgeRegression.Trainer	Trainer for ridge regression.
RLS	Recursive least squares.
RLS.Trainer	Trainer for linear regression by recursive least squares.
SVR<T>	Support vector regression.
SVR.Trainer<T>	Trainer for support vector regression.

Enum Summary

Enum	Description
GradientTreeBoost.Loss	Regression loss function.
NeuralNetwork.ActivationFunction

Package smile.regression Description

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.