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  • 吴裕雄 python 机器学习——线性回归模型

    import numpy as np
    
    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)
    
    #线性回归模型
    def test_LinearRegression(*data):
        X_train,X_test,y_train,y_test=data
        regr = linear_model.LinearRegression()
        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_LinearRegression
    test_LinearRegression(X_train,X_test,y_train,y_test)

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  • 原文地址:https://www.cnblogs.com/tszr/p/10790185.html
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