Package smile.regression
Interface Regression<T>
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- All Superinterfaces:
java.io.Serializable
- All Known Subinterfaces:
OnlineRegression<T>
- All Known Implementing Classes:
GaussianProcessRegression
,NeuralNetwork
,RandomForest
,RegressionTree
public interface Regression<T> extends 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.- Author:
- Haifeng Li
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Method Summary
All Methods Instance Methods Abstract Methods Default Methods Modifier and Type Method Description 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.
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Method Detail
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predict
double predict(T x)
Predicts the dependent variable of an instance.- Parameters:
x
- the instance.- Returns:
- the predicted value of dependent variable.
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predict
default double[] predict(T[] x)
Predicts the dependent variables of an array of instances.- Parameters:
x
- the instances.- Returns:
- the predicted values.
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