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使用python机器学习怎么实现决策树

发表于:2022-12-09 作者:安全数据网编辑
编辑最后更新 2022年12月09日,这期内容当中小编将会给大家带来有关使用python机器学习怎么实现决策树,文章内容丰富且以专业的角度为大家分析和叙述,阅读完这篇文章希望大家可以有所收获。具体内容如下# -*- coding: utf

这期内容当中小编将会给大家带来有关使用python机器学习怎么实现决策树,文章内容丰富且以专业的角度为大家分析和叙述,阅读完这篇文章希望大家可以有所收获。

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 pltimport numpy as npimport pandas as pdimport mglearn from sklearn.model_selection import train_test_split#导入乳腺癌数据集from sklearn.datasets import load_breast_cancerfrom 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:

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