static void crossValidationDecisionTree(String inputFileNameX, String inputFileNameY) {
		System.out.println("DecisitonTree");
		ArrayList<Sample> allSamples = Parser.parse(inputFileNameX, inputFileNameY);
		int[] leafSizes = {100};
		double[] maxTrainEntrs = {0.01, 0.001, 0.0001};
		int[] minDepths = {3};
		
		for (int leafSize : leafSizes) {
			for (double maxTrainEntr : maxTrainEntrs) {
				for (int minDepth : minDepths) {
					Collections.shuffle(allSamples);
					ArrayList<Double> errorRates = new ArrayList<Double>();
					for (int i = 0; i < 10; i++) {
						ArrayList<Sample> trainSample = new ArrayList<Sample>();
						ArrayList<Sample> testSample = new ArrayList<Sample>();
						
						for (int j = 0; j < 345; j++) {
							testSample.add(allSamples.get(j));
						}
						for (int k = 345; k < 3450; k++) {
							trainSample.add(allSamples.get(k));
						}
						
						DecisionTree decisionTree = new DecisionTree(trainSample, leafSize, maxTrainEntr, minDepth, 1);
						decisionTree.classifyAll(testSample);
						double errorRate = getErrorRate(testSample);
						errorRates.add(errorRate);
						
						for (int m = 0; m < 345; m++) {
							allSamples.add(allSamples.remove(0));
						}
					}

					System.out.println(leafSize + ", " + maxTrainEntr + ", " + minDepth + ", " + getAvgErrorRate(errorRates));
					
				}
			}
		}
		
	}