假设通过训练样本生成的决策树为：

{'no surfacing': {0: 'no', 1: {'flippers': {0: 'no', 1: 'yes'}}}}

利用pickle模块可以存储和加载该决策树

tree = {'no surfacing': {0: 'no', 1: {'flippers': {0: 'no', 1: 'yes'}}}}def storeTree(inputTree, filename):import picklefw = open(filename, 'wb')pickle.dump(inputTree, fw)fw.close()def grabTree(filename):import picklefr = open(filename, 'rb')return pickle.load(fr)storeTree(tree, r"D:\picture\tree.txt")
mytree = grabTree(r"D:\picture\tree.txt")print(mytree) # {'no surfacing': {0: 'no', 1: {'flippers': {0: 'no', 1: 'yes'}}}}