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.
- ClassDescriptionBinary sparse linear support vector machines for regression.Regression trait on DataFrame.DataFrameRegression.Trainer<M extends DataFrameRegression>The regression trainer.Elastic Net regularization.Elastic Net hyperparameters.Gaussian Process for Regression.Gaussian process regression hyperparameters.Gradient boosting for regression.Gradient tree boosting hyperparameters.Training status per tree.The learning methods building on kernels.Lasso (least absolute shrinkage and selection operator) regression.Lasso regression hyperparameters.Linear model.Linear support vector machines for regression.Fully connected multilayer perceptron neural network for regression.Ordinary least squares.Computational methods to fit the model.Least squares hyperparameters.Random forest for regression.The base model.Random forest hyperparameters.Training status per tree.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.Regression tree.Regression tree hyperparameters.Ridge Regression.Ridge regression hyperparameters.Sparse linear support vector machines for regression.Epsilon support vector regression.SVM hyperparameters.