Class C45Trainer

  • All Implemented Interfaces:

    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"

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

    Gary Huang
    • 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)