Class CARTJointRegressionTrainer

java.lang.Object
org.tribuo.common.tree.AbstractCARTTrainer<Regressor>
org.tribuo.regression.rtree.CARTJointRegressionTrainer
All Implemented Interfaces:
com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>, DecisionTreeTrainer<Regressor>, SparseTrainer<Regressor>, Trainer<Regressor>, WeightedExamples

public class CARTJointRegressionTrainer extends AbstractCARTTrainer<Regressor>
A Trainer that uses an approximation of the CART algorithm to build a decision tree.

Builds a single tree for all the regression dimensions.

See:

 J. Friedman, T. Hastie, & R. Tibshirani.
 "The Elements of Statistical Learning"
 Springer 2001. PDF
 
  • Constructor Details

    • CARTJointRegressionTrainer

      public CARTJointRegressionTrainer(int maxDepth, float minChildWeight, float minImpurityDecrease, float fractionFeaturesInSplit, boolean useRandomSplitPoints, RegressorImpurity impurity, boolean normalize, long seed)
      Creates a CART Trainer.
      Parameters:
      maxDepth - maxDepth The maximum depth of the tree.
      minChildWeight - minChildWeight The minimum node weight to consider it for a split.
      minImpurityDecrease - The minimum decrease in impurity necessary to split a node.
      fractionFeaturesInSplit - fractionFeaturesInSplit The fraction of features available in each split.
      useRandomSplitPoints - Whether to choose split points for features at random.
      impurity - impurity The impurity function to use to determine split quality.
      normalize - Normalize the leaves so each output sums to one.
      seed - The seed to use for the RNG.
    • CARTJointRegressionTrainer

      public CARTJointRegressionTrainer(int maxDepth, float minChildWeight, float minImpurityDecrease, float fractionFeaturesInSplit, RegressorImpurity impurity, boolean normalize, long seed)
      Creates a CART Trainer.

      Computes the exact split point.

      Parameters:
      maxDepth - maxDepth The maximum depth of the tree.
      minChildWeight - minChildWeight The minimum node weight to consider it for a split.
      minImpurityDecrease - The minimum decrease in impurity necessary to split a node.
      fractionFeaturesInSplit - fractionFeaturesInSplit The fraction of features available in each split.
      impurity - impurity The impurity function to use to determine split quality.
      normalize - Normalize the leaves so each output sums to one.
      seed - The seed to use for the RNG.
    • CARTJointRegressionTrainer

      public CARTJointRegressionTrainer()
      Creates a CART Trainer.

      Sets the impurity to the MeanSquaredError, computes an arbitrary depth tree with exact split points using all the features, and does not normalize the outputs.

    • CARTJointRegressionTrainer

      public CARTJointRegressionTrainer(int maxDepth)
      Creates a CART Trainer.

      Sets the impurity to the MeanSquaredError, computes the exact split points using all the features, and does not normalize the outputs.

      Parameters:
      maxDepth - The maximum depth of the tree.
    • CARTJointRegressionTrainer

      public CARTJointRegressionTrainer(int maxDepth, boolean normalize)
      Creates a CART Trainer. Sets the impurity to the MeanSquaredError.
      Parameters:
      maxDepth - The maximum depth of the tree.
      normalize - Normalises the leaves so each leaf has a distribution which sums to 1.0.
  • Method Details