EL之Bagging：利用Bagging算法实现回归预测(实数值评分预测)问题
目录
输出结果
设计思路
核心思路
输出结果
设计思路
核心思路
#4.1、当treeDepth=1，对图进行可视化#(1)、定义numTreesMax、treeDepthnumTreesMax = 30treeDepth = 1 # ----------------------▲▲▲▲▲modelList = []predList = []#number of samples to draw for stochastic baggingnBagSamples = int(len(xTrain) * 0.5)for iTrees in range(numTreesMax):idxBag = []for i in range(nBagSamples):idxBag.append(random.choice(range(len(xTrain))))xTrainBag = [xTrain[i] for i in idxBag]yTrainBag = [yTrain[i] for i in idxBag]modelList.append(DecisionTreeRegressor(max_depth=treeDepth))modelList[-1].fit(xTrainBag, yTrainBag)latestPrediction = modelList[-1].predict(xTest)predList.append(list(latestPrediction))mse = []allPredictions = []for iModels in range(len(modelList)):prediction = []for iPred in range(len(xTest)):prediction.append(sum([predList[i][iPred] for i in range(iModels + 1)])/(iModels + 1))allPredictions.append(prediction)errors = [(yTest[i] - prediction[i]) for i in range(len(yTest))]mse.append(sum([e * e for e in errors]) / len(yTest))#4.2、当treeDepth=1，对图进行可视化#(1)、定义numTreesMax、treeDepthnumTreesMax = 30treeDepth = 5 # ----------------------▲▲▲▲▲#4.3、当treeDepth=12，对图进行可视化#(1)、定义numTreesMax、treeDepthnumTreesMax = 100 # ----------------------☆☆☆☆☆treeDepth = 12# ----------------------☆☆☆☆☆
