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本文实例为大家分享了python机器学习实现决策树的具体代码,供大家参考,具体内容如下
# -*- coding: utf-8 -*- """ Created on Sat Nov 9 10:42:38 2019 @author: asus """ """ 决策树 目的: 1. 使用决策树模型 2. 了解决策树模型的参数 3. 初步了解调参数 要求: 基于乳腺癌数据集完成以下任务: 1.调整参数criterion,使用不同算法信息熵(entropy)和基尼不纯度算法(gini) 2.调整max_depth参数值,查看不同的精度 3.根据参数criterion和max_depth得出你初步的结论。 """ import matplotlib.pyplot as plt import numpy as np import pandas as pd import mglearn from sklearn.model_selection import train_test_split #导入乳腺癌数据集 from sklearn.datasets import load_breast_cancer from sklearn.tree import DecisionTreeClassifier #决策树并非深度越大越好,考虑过拟合的问题 #mglearn.plots.plot_animal_tree() #mglearn.plots.plot_tree_progressive() #获取数据集 cancer = load_breast_cancer() #对数据集进行切片 X_train,X_test,y_train,y_test = train_test_split(cancer.data,cancer.target, stratify = cancer.target,random_state = 42) #查看训练集和测试集数据 print('train dataset :{0} ;test dataset :{1}'.format(X_train.shape,X_test.shape)) #建立模型(基尼不纯度算法(gini)),使用不同最大深度和随机状态和不同的算法看模型评分 tree = DecisionTreeClassifier(random_state = 0,criterion = 'gini',max_depth = 5) #训练模型 tree.fit(X_train,y_train) #评估模型 print("Accuracy(准确性) on training set: {:.3f}".format(tree.score(X_train, y_train))) print("Accuracy(准确性) on test set: {:.3f}".format(tree.score(X_test, y_test))) print(tree) # 参数选择 max_depth,算法选择基尼不纯度算法(gini) or 信息熵(entropy) def Tree_score(depth = 3,criterion = 'entropy'): """ 参数为max_depth(默认为3)和criterion(默认为信息熵entropy), 函数返回模型的训练精度和测试精度 """ tree = DecisionTreeClassifier(criterion = criterion,max_depth = depth) tree.fit(X_train,y_train) train_score = tree.score(X_train, y_train) test_score = tree.score(X_test, y_test) return (train_score,test_score) #gini算法,深度对模型精度的影响 depths = range(2,25)#考虑到数据集有30个属性 scores = [Tree_score(d,'gini') for d in depths] train_scores = [s[0] for s in scores] test_scores = [s[1] for s in scores] plt.figure(figsize = (6,6),dpi = 144) plt.grid() plt.xlabel("max_depth of decision Tree") plt.ylabel("score") plt.title("'gini'") plt.plot(depths,train_scores,'.g-',label = 'training score') plt.plot(depths,test_scores,'.r--',label = 'testing score') plt.legend() #信息熵(entropy),深度对模型精度的影响 scores = [Tree_score(d) for d in depths] train_scores = [s[0] for s in scores] test_scores = [s[1] for s in scores] plt.figure(figsize = (6,6),dpi = 144) plt.grid() plt.xlabel("max_depth of decision Tree") plt.ylabel("score") plt.title("'entropy'") plt.plot(depths,train_scores,'.g-',label = 'training score') plt.plot(depths,test_scores,'.r--',label = 'testing score') plt.legend()
运行结果: