def testSetM(self):
decisionTree = DecisionTree()
decisionTree.setMinSplit(5)
folds = 3
meanError, varError = decisionTree.evaluateCv(self.X, self.y, folds)
decisionTree.setM(100)
#decisionTree.setMinSplit(20)
meanError2, varError = decisionTree.evaluateCv(self.X, self.y, folds)
self.assertTrue(meanError != meanError2)
def testSetM(self):
decisionTree = DecisionTree()
decisionTree.setMinSplit(5)
folds = 3
meanError, varError = decisionTree.evaluateCv(self.X, self.y, folds)
decisionTree.setM(100)
#decisionTree.setMinSplit(20)
meanError2, varError = decisionTree.evaluateCv(self.X, self.y, folds)
self.assertTrue(meanError != meanError2)
def testMinSplit(self):
decisionTree = DecisionTree()
decisionTree.setMinSplit(20)
decisionTree.learnModel(self.X, self.y)
size = orngTree.countNodes(decisionTree.getClassifier())
#orngTree.printTree(decisionTree.getClassifier())
decisionTree.setMinSplit(0)
decisionTree.learnModel(self.X, self.y)
size2 = orngTree.countNodes(decisionTree.getClassifier())
#orngTree.printTree(decisionTree.getClassifier())
self.assertTrue(size < size2)
def testMinSplit(self):
decisionTree = DecisionTree()
decisionTree.setMinSplit(20)
decisionTree.learnModel(self.X, self.y)
size = orngTree.countNodes(decisionTree.getClassifier())
#orngTree.printTree(decisionTree.getClassifier())
decisionTree.setMinSplit(0)
decisionTree.learnModel(self.X, self.y)
size2 = orngTree.countNodes(decisionTree.getClassifier())
#orngTree.printTree(decisionTree.getClassifier())
self.assertTrue(size < size2)
