• 吴裕雄 python 机器学习——集成学习随机森林RandomForestClassifier分类模型


    import numpy as np
    import matplotlib.pyplot as plt
    
    from sklearn import datasets,ensemble
    from sklearn.model_selection import train_test_split
    
    def load_data_classification():
        '''
        加载用于分类问题的数据集
        '''
        # 使用 scikit-learn 自带的 digits 数据集
        digits=datasets.load_digits() 
        # 分层采样拆分成训练集和测试集,测试集大小为原始数据集大小的 1/4
        return train_test_split(digits.data,digits.target,test_size=0.25,random_state=0,stratify=digits.target) 
    
    #集成学习随机森林RandomForestClassifier分类模型
    def test_RandomForestClassifier(*data):
        X_train,X_test,y_train,y_test=data
        clf=ensemble.RandomForestClassifier()
        clf.fit(X_train,y_train)
        print("Traing Score:%f"%clf.score(X_train,y_train))
        print("Testing Score:%f"%clf.score(X_test,y_test))
        
    # 获取分类数据
    X_train,X_test,y_train,y_test=load_data_classification() 
    # 调用 test_RandomForestClassifier
    test_RandomForestClassifier(X_train,X_test,y_train,y_test) 

    def test_RandomForestClassifier_num(*data):
        '''
        测试 RandomForestClassifier 的预测性能随 n_estimators 参数的影响
        '''
        X_train,X_test,y_train,y_test=data
        nums=np.arange(1,100,step=2)
        fig=plt.figure()
        ax=fig.add_subplot(1,1,1)
        testing_scores=[]
        training_scores=[]
        for num in nums:
            clf=ensemble.RandomForestClassifier(n_estimators=num)
            clf.fit(X_train,y_train)
            training_scores.append(clf.score(X_train,y_train))
            testing_scores.append(clf.score(X_test,y_test))
        ax.plot(nums,training_scores,label="Training Score")
        ax.plot(nums,testing_scores,label="Testing Score")
        ax.set_xlabel("estimator num")
        ax.set_ylabel("score")
        ax.legend(loc="lower right")
        ax.set_ylim(0,1.05)
        plt.suptitle("RandomForestClassifier")
        plt.show()
        
    # 调用 test_RandomForestClassifier_num
    test_RandomForestClassifier_num(X_train,X_test,y_train,y_test) 

    def test_RandomForestClassifier_max_depth(*data):
        '''
        测试 RandomForestClassifier 的预测性能随 max_depth 参数的影响
        '''
        X_train,X_test,y_train,y_test=data
        maxdepths=range(1,20)
        fig=plt.figure()
        ax=fig.add_subplot(1,1,1)
        testing_scores=[]
        training_scores=[]
        for max_depth in maxdepths:
            clf=ensemble.RandomForestClassifier(max_depth=max_depth)
            clf.fit(X_train,y_train)
            training_scores.append(clf.score(X_train,y_train))
            testing_scores.append(clf.score(X_test,y_test))
        ax.plot(maxdepths,training_scores,label="Training Score")
        ax.plot(maxdepths,testing_scores,label="Testing Score")
        ax.set_xlabel("max_depth")
        ax.set_ylabel("score")
        ax.legend(loc="lower right")
        ax.set_ylim(0,1.05)
        plt.suptitle("RandomForestClassifier")
        plt.show()
        
    # 调用 test_RandomForestClassifier_max_depth
    test_RandomForestClassifier_max_depth(X_train,X_test,y_train,y_test) 

    def test_RandomForestClassifier_max_features(*data):
        '''
        测试 RandomForestClassifier 的预测性能随 max_features 参数的影响
        '''
        X_train,X_test,y_train,y_test=data
        max_features=np.linspace(0.01,1.0)
        fig=plt.figure()
        ax=fig.add_subplot(1,1,1)
        testing_scores=[]
        training_scores=[]
        for max_feature in max_features:
            clf=ensemble.RandomForestClassifier(max_features=max_feature)
            clf.fit(X_train,y_train)
            training_scores.append(clf.score(X_train,y_train))
            testing_scores.append(clf.score(X_test,y_test))
        ax.plot(max_features,training_scores,label="Training Score")
        ax.plot(max_features,testing_scores,label="Testing Score")
        ax.set_xlabel("max_feature")
        ax.set_ylabel("score")
        ax.legend(loc="lower right")
        ax.set_ylim(0,1.05)
        plt.suptitle("RandomForestClassifier")
        plt.show()
            
    # 调用 test_RandomForestClassifier_max_features
    test_RandomForestClassifier_max_features(X_train,X_test,y_train,y_test) 

  • 相关阅读:
    解决小程序sessionid不一致
    小程序实现写入缓存与读取缓存
    小程序登录时如何获取input框中的内容
    js实现截取字符串后几位
    js用正则判断身份证号码
    sublime Text3安装及配置与解决安装插件失败
    jQuery实现全选与全部选
    jQuery实现enter键登录
    常用正则表达式的判断与写法
    js实现正则判断手机号
  • 原文地址:https://www.cnblogs.com/tszr/p/10801588.html
Copyright © 2020-2023  润新知