All Classes and Interfaces

Class	Description
CARTJointRegressionTrainer	A Trainer that uses an approximation of the CART algorithm to build a decision tree.
CARTRegressionTrainer	A Trainer that uses an approximation of the CART algorithm to build a decision tree.
IndependentRegressionTreeModel	A Model wrapped around a list of decision tree root Nodes used to generate independent predictions for each dimension in a regression.
InvertedFeature	Internal datastructure for implementing a decision tree.
JointRegressorTrainingNode	A decision tree node used at training time.
MeanAbsoluteError	Measures the mean absolute error over a set of inputs.
MeanSquaredError	Measures the mean squared error over a set of inputs.
RegressorImpurity	Calculates a tree impurity score based on the regression targets.
RegressorImpurity.ImpurityTuple	Tuple class for the impurity and summed weight.
RegressorTrainingNode	A decision tree node used at training time.
RegressorTrainingNode.InvertedData	Tuple containing an inverted dataset (i.e., feature-wise not exmaple-wise).
TrainTest	Build and run a regression tree for a standard dataset.
TrainTest.ImpurityType	Impurity function.
TrainTest.RegressionTreeOptions	Command line options.
TrainTest.TreeType	Type of tree trainer.
TreeFeature	An inverted feature, which stores a reference to all the values of this feature.