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)