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  • 吴裕雄--天生自然 人工智能机器学习实战代码:岭回归

    import numpy as np
    import matplotlib.pyplot as plt
    
    from sklearn import datasets, linear_model
    from sklearn.model_selection import train_test_split
    
    def load_data():
        diabetes = datasets.load_diabetes()
        return train_test_split(diabetes.data,diabetes.target,test_size=0.25,random_state=0)
    
    #岭回归
    def test_Ridge(*data):
        X_train,X_test,y_train,y_test=data
        regr = linear_model.Ridge()
        regr.fit(X_train, y_train)
        print('Coefficients:%s, intercept %.2f'%(regr.coef_,regr.intercept_))
        print("Residual sum of squares: %.2f"% np.mean((regr.predict(X_test) - y_test) ** 2))
        print('Score: %.2f' % regr.score(X_test, y_test))
    
    # 产生用于回归问题的数据集
    X_train,X_test,y_train,y_test=load_data() 
     # 调用 test_Ridge
    test_Ridge(X_train,X_test,y_train,y_test)
    
    def test_Ridge_alpha(*data):
        X_train,X_test,y_train,y_test=data
        alphas=[0.01,0.02,0.05,0.1,0.2,0.5,1,2,5,10,20,50,100,200,500,1000]
        scores=[]
        for i,alpha in enumerate(alphas):
            regr = linear_model.Ridge(alpha=alpha)
            regr.fit(X_train, y_train)
            scores.append(regr.score(X_test, y_test))
        ## 绘图
        fig=plt.figure()
        ax=fig.add_subplot(1,1,1)
        ax.plot(alphas,scores)
        ax.set_xlabel(r"$alpha$")
        ax.set_ylabel(r"score")
        ax.set_xscale('log')
        ax.set_title("Ridge")
        plt.show()
        
    test_Ridge_alpha(X_train,X_test,y_train,y_test) # 调用 test_Ridge_alpha

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  • 原文地址:https://www.cnblogs.com/tszr/p/11177879.html
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