Class HoeffdingOptionTree

  • All Implemented Interfaces:
    Configurable, Serializable, CapabilitiesHandler, Classifier, MultiClassClassifier, AWTRenderable, Learner<Example<Instance>>, MOAObject, OptionHandler
    Direct Known Subclasses:
    AdaHoeffdingOptionTree

    public class HoeffdingOptionTree
    extends AbstractClassifier
    implements MultiClassClassifier, CapabilitiesHandler
    Hoeffding Option Tree.

    Hoeffding Option Trees are regular Hoeffding trees containing additional option nodes that allow several tests to be applied, leading to multiple Hoeffding trees as separate paths. They consist of a single structure that efficiently represents multiple trees. A particular example can travel down multiple paths of the tree, contributing, in different ways, to different options.

    See for details:

    B. Pfahringer, G. Holmes, and R. Kirkby. New options for hoeffding trees. In AI, pages 90–99, 2007.

    Parameters:

    • -o : Maximum number of option paths per node
    • -m : Maximum memory consumed by the tree
    • -n : Numeric estimator to use :
      • Gaussian approximation evaluating 10 splitpoints
      • Gaussian approximation evaluating 100 splitpoints
      • Greenwald-Khanna quantile summary with 10 tuples
      • Greenwald-Khanna quantile summary with 100 tuples
      • Greenwald-Khanna quantile summary with 1000 tuples
      • VFML method with 10 bins
      • VFML method with 100 bins
      • VFML method with 1000 bins
      • Exhaustive binary tree
    • -e : How many instances between memory consumption checks
    • -g : The number of instances a leaf should observe between split attempts
    • -s : Split criterion to use. Example : InfoGainSplitCriterion
    • -c : The allowable error in split decision, values closer to 0 will take longer to decide
    • -w : The allowable error in secondary split decisions, values closer to 0 will take longer to decide
    • -t : Threshold below which a split will be forced to break ties
    • -b : Only allow binary splits
    • -z : Memory strategy to use
    • -r : Disable poor attributes
    • -p : Disable pre-pruning
    • -d : File to append option table to.
    • -l : Leaf prediction to use: MajorityClass (MC), Naive Bayes (NB) or NaiveBayes adaptive (NBAdaptive).
    • -q : The number of instances a leaf should observe before permitting Naive Bayes
    Version:
    $Revision: 7 $
    Author:
    Richard Kirkby (rkirkby@cs.waikato.ac.nz)
    See Also:
    Serialized Form
    • Field Detail

      • maxOptionPathsOption

        public IntOption maxOptionPathsOption
      • maxByteSizeOption

        public IntOption maxByteSizeOption
      • numericEstimatorOption

        public ClassOption numericEstimatorOption
      • nominalEstimatorOption

        public ClassOption nominalEstimatorOption
      • memoryEstimatePeriodOption

        public IntOption memoryEstimatePeriodOption
      • gracePeriodOption

        public IntOption gracePeriodOption
      • splitCriterionOption

        public ClassOption splitCriterionOption
      • splitConfidenceOption

        public FloatOption splitConfidenceOption
      • secondarySplitConfidenceOption

        public FloatOption secondarySplitConfidenceOption
      • tieThresholdOption

        public FloatOption tieThresholdOption
      • binarySplitsOption

        public FlagOption binarySplitsOption
      • removePoorAttsOption

        public FlagOption removePoorAttsOption
      • noPrePruneOption

        public FlagOption noPrePruneOption
      • dumpFileOption

        public FileOption dumpFileOption
      • memoryStrategyOption

        public IntOption memoryStrategyOption
      • decisionNodeCount

        protected int decisionNodeCount
      • activeLeafNodeCount

        protected int activeLeafNodeCount
      • inactiveLeafNodeCount

        protected int inactiveLeafNodeCount
      • inactiveLeafByteSizeEstimate

        protected double inactiveLeafByteSizeEstimate
      • activeLeafByteSizeEstimate

        protected double activeLeafByteSizeEstimate
      • byteSizeEstimateOverheadFraction

        protected double byteSizeEstimateOverheadFraction
      • maxPredictionPaths

        protected int maxPredictionPaths
      • nbThresholdOption

        public IntOption nbThresholdOption
    • Constructor Detail

      • HoeffdingOptionTree

        public HoeffdingOptionTree()