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EL之Bagging:利用DIY数据集(预留30%数据+两种树深)训练Bagging算法(DTR)
输出结果
1、treeDepth=1
2、treeDepth=5
设计思路
核心代码
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))