9.3将CART算法用于回归
在python中可以直接以字典这个数据结构来实现树的生成
而且CART是给定切分特征值，用来划分特征集是归于左边还是右边
def binSplitDataSet(dataSet, feature, value):#数据集合，待切分特征和特征的值 mat0 = dataSet[nonzero(dataSet[:,feature] > value)[0],:][0] mat1 = dataSet[nonzero(dataSet[:,feature] <= value)[0],:][0] return mat0,mat1#返回两个子集 def regLeaf(dataSet):#生成叶子节点 return mean(dataSet[:,-1]) def regErr(dataSet):#计算目标变量的平方误差 return var(dataSet[:,-1]) * shape(dataSet)[0] #均方差*样本个数 def chooseBestSplit(dataSet, leafType=regLeaf, errType=regErr, ops=(1,4)): tolS = ops[0]; tolN = ops[1] #tolS是容许的误差下降至，tolN是切分的最少样本数 if len(set(dataSet[:,-1].T.tolist()[0])) == 1: #只有一个样本停止切分，加入叶子节点 return None, leafType(dataSet) m,n = shape(dataSet) #选择最好的切分方式 S = errType(dataSet) bestS = inf; bestIndex = 0; bestValue = 0 for featIndex in range(n-1): for splitVal in set(dataSet[:,featIndex]): mat0, mat1 = binSplitDataSet(dataSet, featIndex, splitVal) if (shape(mat0)[0] < tolN) or (shape(mat1)[0] < tolN): continue newS = errType(mat0) + errType(mat1) if newS < bestS: bestIndex = featIndex bestValue = splitVal bestS = newS #如果没有找到最好的切分方式，则停止切分 if (S - bestS) < tolS: return None, leafType(dataSet) #exit cond 2 mat0, mat1 = binSplitDataSet(dataSet, bestIndex, bestValue) if (shape(mat0)[0] < tolN) or (shape(mat1)[0] < tolN): #exit cond 3 return None, leafType(dataSet) return bestIndex,bestValue