Class C45Trainer

  • All Implemented Interfaces:
    Boostable

    public class C45Trainer
    extends ClassifierTrainer<C45>
    implements Boostable
    A C4.5 decision tree learner, approximtely. Currently treats all features as continuous-valued, and has no notion of missing values.

    This implementation uses MDL for pruning.

    J. R. Quinlan
    "Improved Use of Continuous Attributes in C4.5"
    ftp://ftp.cs.cmu.edu/project/jair/volume4/quinlan96a.ps

    J. R. Quinlan and R. L. Rivest
    "Inferring Decision Trees Using Minimum Description Length Principle"

    Author:
    Gary Huang ghuang@cs.umass.edu
    • Constructor Detail

      • C45Trainer

        public C45Trainer()
        Uses default values: not depth limited tree with a minimum of 2 instances in each leaf node
      • C45Trainer

        public C45Trainer​(int maxDepth)
        Construct a depth-limited tree with the given depth limit
      • C45Trainer

        public C45Trainer​(boolean doPruning)
      • C45Trainer

        public C45Trainer​(int maxDepth,
                          boolean doPruning)
    • Method Detail

      • setDoPruning

        public void setDoPruning​(boolean doPruning)
      • getDoPruning

        public boolean getDoPruning()
      • setDepthLimited

        public void setDepthLimited​(boolean depthLimited)
      • getDepthLimited

        public boolean getDepthLimited()
      • setMaxDepth

        public void setMaxDepth​(int maxDepth)
      • getMaxDepth

        public int getMaxDepth()
      • setMinNumInsts

        public void setMinNumInsts​(int minNumInsts)
      • getMinNumInsts

        public int getMinNumInsts()
      • splitTree

        protected void splitTree​(C45.Node node,
                                 int depth)