算法梳理进阶线性回归(三)

向右看齐 2022-03-14 08:22 208阅读 0赞

### 文章目录 ###

*  代码实现
 *  效果

# 代码实现 #

# -*- coding:utf-8 -*-
    
    import sklearn.datasets as datasets
    from sklearn.linear_model import LinearRegression
    from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
    from sklearn.model_selection import train_test_split
    import numpy as np
    
    
    class HousePrice:
    
        def data_split(self):
            # 载入数据集
            boston_data = datasets.load_boston()
            # 得到RM列的数据
            x = boston_data.data[:, 5]
    
            # 变为一列
            x = x.reshape(-1, 1)
            y = boston_data.target
            y = y.reshape(-1, 1)
    
            # 分割数据集
            x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)
    
            # 创建线性回归模型
            lr = LinearRegression()
            lr.fit(x_train, y_train)
    
            # 得到预测结果
            y_pred = lr.predict(x_test)
            return y_pred, y_test
    
    
    if __name__ == '__main__':
        hp = HousePrice()
        # 得到预测以及测试原本的结果
        y_pred, y_test = hp.data_split()
    
        # 计算评分指标
        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
        r_score = 1 - (mse_test / np.var(y_test))
        print('自己实现')
        print("均方误差:{0},平均绝对误差:{1},均方根误差:{2},可决系数:{3}".format(mse_test, mae_test, rmse_test, r2_score))
    
        # 调用函数得到
        # 调用函数获得结果
        mse_test = mean_squared_error(y_test, y_pred)
        mae_test = mean_absolute_error(y_test, y_pred)
        rmse_test = mse_test ** 0.5
        r2_score = r2_score(y_test, y_pred)
    
        print('调用函数')
        print("均方误差:{0},平均绝对误差:{1},均方根误差:{2},可决系数:{3}".format(mse_test, mae_test, rmse_test, r2_score))
        pass

# 效果 #

![在这里插入图片描述][20190303225848962.png]

[20190303225848962.png]: /images/20220313/2c875661492b46f088d620bd3d69969b.png