Interface DecisionTreeTrainer<T extends Output<T>>

All Superinterfaces:
com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>, SparseTrainer<T>, Trainer<T>, WeightedExamples
All Known Implementing Classes:
AbstractCARTTrainer, CARTClassificationTrainer, CARTJointRegressionTrainer, CARTRegressionTrainer
public interface DecisionTreeTrainer<T extends Output<T>> extends SparseTrainer<T>, WeightedExamples
A tag interface for a Trainer so the random forests trainer can check if it's actually a tree.

  • Field Summary

    Fields inherited from interface org.tribuo.Trainer

    DEFAULT_SEED

  • Method Summary

    Modifier and Type
    Method
    Description
    float
    getFractionFeaturesInSplit()
    Returns the feature subsampling rate.
    float
    getMinImpurityDecrease()
    Returns the minimum decrease in impurity necessary to split a node.
    boolean
    getUseRandomSplitPoints()
    Returns whether to choose split points for features at random.

    Methods inherited from interface com.oracle.labs.mlrg.olcut.config.Configurable

    postConfig

    Methods inherited from interface com.oracle.labs.mlrg.olcut.provenance.Provenancable

    getProvenance

    Methods inherited from interface org.tribuo.SparseTrainer

    train, train

    Methods inherited from interface org.tribuo.Trainer

    getInvocationCount

  • Method Details

    • getFractionFeaturesInSplit

      float getFractionFeaturesInSplit()
      Returns the feature subsampling rate.
      Returns:
      The feature subsampling rate.

    • getUseRandomSplitPoints

      boolean getUseRandomSplitPoints()
      Returns whether to choose split points for features at random.
      Returns:
      Whether to choose split points for features at random.

    • getMinImpurityDecrease

      float getMinImpurityDecrease()
      Returns the minimum decrease in impurity necessary to split a node.
      Returns:
      The minimum decrease in impurity necessary to split a node.