Package jsat.regression

Interface Summary

Interface	Description
OrdinaryKriging.Variogram
Regressor
UpdateableRegressor	UpdateableRegressor is an interface for one type of Online learner.
WarmRegressor	This interface is meant for models that support efficient warm starting from the solution of a previous model.

Class Summary

Class	Description
AveragedRegressor	Creates a regressor that averages the results of several voting regression methods.
BaseUpdateableRegressor	A base implementation of the UpdateableRegressor.
KernelRidgeRegression	A kernelized implementation of Ridge Regression.
KernelRLS	Provides an implementation of the Kernel Recursive Least Squares algorithm.
LogisticRegression	Logistic regression is a common method used to fit a probability between binary outputs.
MultipleLinearRegression
NadarayaWatson	The Nadaraya-Watson regressor uses the Kernel Density Estimator to perform regression on a data set.
OrdinaryKriging	An implementation of Ordinary Kriging with support for a uniform error measurement.
OrdinaryKriging.PowVariogram
RANSAC	RANSAC is a randomized meta algorithm that is useful for fitting a model to a data set that has a large amount of outliers that do not represent the true distribution well.
RegressionDataSet	A RegressionDataSet is a data set specifically for the task of performing regression.
RegressionModelEvaluation	Provides a mechanism to quickly evaluate a regression model on a data set.
RidgeRegression	An implementation of Ridge Regression that finds the exact solution.
StochasticGradientBoosting	An implementation of Stochastic Gradient Boosting (SGB) for the Squared Error loss.
StochasticRidgeRegression	A Stochastic implementation of Ridge Regression.

Enum Summary

Enum	Description
RidgeRegression.SolverMode	Sets which solver to use