- Type Parameters:
InputType - The type of input data in the input-output pair that
the learner can learn from. The Evaluator learned from the
algorithm also takes this as the input parameter.EvaluatorType - The type of object created by the learning algorithm.
- All Superinterfaces:
- BatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>>,EvaluatorType>, java.lang.Cloneable, CloneableSerializable, Regression<InputType,java.lang.Double,EvaluatorType>, java.io.Serializable, SupervisedBatchLearner<InputType,java.lang.Double,EvaluatorType>
@PublicationReference(author="Wikipedia",title="Simple linear regression",type=WebPage,year=2012,url="http://en.wikipedia.org/wiki/Simple_regression") @PublicationReference(author="StatSoft",title="Multiple Regression",type=WebPage,url="http://www.statsoft.com/textbook/multiple-regression/",year=2012)
public interface UnivariateRegression<InputType,EvaluatorType extends Evaluator<? super InputType,? extends java.lang.Double>>
extends Regression<InputType,java.lang.Double,EvaluatorType>
A type of Regression algorithm that has a single dependent (output) variable
that we are trying to predict. This formulation allows for single
independent input variable (simple regression) or multiple input variables
(multiple regression) onto a single dependent (output) variable.
- Since:
- 3.4.2
- Author:
- Kevin R. Dixon