def testCar(setFunc = setEntropy, infoFunc = infoGain):
"""Correct classification averate rate is about 0.89"""
examples,attrValues,labelName,labelValues = getCarDataset()
print 'Testing Car dataset. Number of examples %d.'%len(examples)
tree = makeTree(examples, attrValues, labelName, setFunc, infoFunc)
f = open('car.out','w')
f.write(str(tree))
f.close()
print 'Tree size: %d.\n'%tree.count()
print 'Entire tree written out to car.out in local directory\n'
dataset = getCarDataset()
evaluation = getAverageClassificaionRate((examples,attrValues,labelName,labelValues))
printDemarcation()
return (tree,evaluation)
def testCar(setFunc = setEntropy, infoFunc = infoGain):
"""Correct classification averate rate is about 0.95"""
examples,attrValues,labelName,labelValues = getCarDataset()
print 'Testing Car dataset. Number of examples %d.'%len(examples)
tree = makeTree(examples, attrValues, labelName, setFunc, infoFunc)
f = open('car.out','w')
f.write(str(tree))
f.close()
print 'Tree size: %d.\n'%tree.count()
print 'Entire tree written out to car.out in local directory\n'
dataset = getCarDataset()
evaluation = getAverageClassificaionRate((examples,attrValues,labelName,labelValues))
print 'Results for training set:\n%s\n'%str(evaluation)
printDemarcation()
return (tree,evaluation)
