edu.umass.cs.mallet.base.classify
Class DecisionTreeTrainer

java.lang.Object
  extended byedu.umass.cs.mallet.base.classify.ClassifierTrainer
      extended byedu.umass.cs.mallet.base.classify.DecisionTreeTrainer
All Implemented Interfaces:
Boostable

public class DecisionTreeTrainer
extends ClassifierTrainer
implements Boostable

A decision tree learner, roughly ID3. Does not yet implement splitting of continuous-valued features, but it should in the future. Currently a feature is considered "present" if it has positive value. ftp://ftp.cs.cmu.edu/project/jair/volume4/quinlan96a.ps Only set up for conventiently learning decision stubs: there is no pruning or good stopping rule. Currently only stop by reaching a maximum depth.


Constructor Summary
DecisionTreeTrainer()
           
DecisionTreeTrainer(int maxDepth)
           
 
Method Summary
protected  void splitTree(DecisionTree.Node node, FeatureSelection selectedFeatures, int depth)
           
 Classifier train(InstanceList trainingList, InstanceList validationList, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier)
          Return a new classifier tuned using the three arguments.
 
Methods inherited from class edu.umass.cs.mallet.base.classify.ClassifierTrainer
main, toString, train, train, train, train
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

Constructor Detail

DecisionTreeTrainer

public DecisionTreeTrainer(int maxDepth)

DecisionTreeTrainer

public DecisionTreeTrainer()
Method Detail

splitTree

protected void splitTree(DecisionTree.Node node,
                         FeatureSelection selectedFeatures,
                         int depth)

train

public Classifier train(InstanceList trainingList,
                        InstanceList validationList,
                        InstanceList testSet,
                        ClassifierEvaluating evaluator,
                        Classifier initialClassifier)
Description copied from class: ClassifierTrainer
Return a new classifier tuned using the three arguments.

Specified by:
train in class ClassifierTrainer
Parameters:
trainingList - examples used to set parameters.
validationList - examples used to tune meta-parameters. May be null.
testSet - examples not examined at all for training, but passed on to diagnostic routines. May be null.
initialClassifier - training process may start from here. The parameters of the initialClassifier are not modified. May be null.