smile.regression

Interface Regression<T>

All Superinterfaces:

java.io.Serializable, java.util.function.ToDoubleFunction<T>

All Known Subinterfaces:

OnlineRegression<T>

All Known Implementing Classes:

GradientTreeBoost, KernelMachine, LinearModel, MLP, RandomForest, RBFNetwork, RegressionTree

public interface Regression<T>
extends java.util.function.ToDoubleFunction<T>, java.io.Serializable

Regression analysis includes any techniques for modeling and analyzing 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. Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning.

Method Summary

Modifier and Type	Method and Description
default double	applyAsDouble(T x)
double	predict(T x) Predicts the dependent variable of an instance.
default double[]	predict(T[] x) Predicts the dependent variables of an array of instances.

Method Detail

predict

double predict(T x)

Predicts the dependent variable of an instance.

Parameters:

x - an instance.

Returns:

the predicted value of dependent variable.

predict

default double[] predict(T[] x)

Predicts the dependent variables of an array of instances.

Parameters:

x - the instances.

Returns:

the predicted values.

applyAsDouble

default double applyAsDouble(T x)

Specified by:

applyAsDouble in interface java.util.function.ToDoubleFunction<T>