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    python机器学习实现决策树

    栏目:Linux/apache问题 时间:2019-11-12 10:23

    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:

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