Package cc.mallet.classify

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)