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线性回归算法梳理3

线性回归算法梳理3

作者: Acapella_Zhang | 来源:发表于2019-03-03 23:16 被阅读0次

    对波士顿的房价进行预测

    1.数据集的载入

    x = boston.data[:,5]#得到rm列
    x = x.reshape(-1,1)
    y = boston.target
    y = y.reshape(-1,1)
    

    2.数据切分

    from sklearn.model_selection import train_test_split
    #分割数据集
    x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.25,random_state=0)
    

    3.进行回归预测

    from sklearn.linear_model import LinearRegression
    #创建回归模型
    regr = LinearRegression()
    regr.fit(x_train,y_train)
    
    y_pred = regr.predict(x_test)
    

    4.对评价标准进行计算

    #根据公式计算结果
    mse_test = np.sum((y_pred-y_test)**2)/len(y_test)
    mae_test = np.sum(np.absolute(y_pred-y_test))/len(y_test)
    rmse_test = mse_test**0.5
    r2_score = 1-(mse_test/np.var(y_test))
    

    再使用sklearn进行计算

    from sklearn.metrics import mean_squared_error
    from sklearn.metrics import mean_absolute_error
    from sklearn.metrics import r2_score #R square
    
    mse_test1 = mean_squared_error(y_test,y_pred)
    mae_test1 = mean_absolute_error(y_test,y_pred)
    rmse_test1 = mse_test1**0.5
    r2_score1 = r2_score(y_test,y_pred)
    

    得到最终的r2_score为

    0.4679000543136782

    可见拟合效果不是特别好

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