sklearn完成标准线性回归

迈不过友情╰ 2023-07-25 06:09 47阅读 0赞

import numpy as np
    import matplotlib.pyplot as plt
    import pandas as pd
    from sklearn.model_selection import train_test_split #模型评估 留出法
    from sklearn.linear_model import LinearRegression#线性回归
    from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score#评估  mse   mae  r2

![在这里插入图片描述][20200413060357267.png]

path = r'../datas/Advertising.csv'
    
    # pandas读入
    data = pd.read_csv(path)    # 4列数据
    x = data[['TV', 'Radio', 'Newspaper']]#将电视广告，广播广告，报纸广告作为特征
    y = data['Sales']#销售额作为标签
    print(x)
    print(y)

# # 绘制样本分布，观察效果（其实报纸不适合用作考虑特征）
    plt.plot(data['TV'], y, 'ro', label='TV')
    plt.plot(data['Radio'], y, 'g*', label='Radio')
    plt.plot(data['Newspaper'], y, 'bv', label='Newspaer')
    plt.legend(loc='lower right')
    plt.grid()
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzY3MzE1Ng_size_16_color_FFFFFF_t_70]

#留出法，进行分割，默认比例7.5:2.5
    x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=1)# 随机种子
    linreg = LinearRegression()
    model = linreg.fit(x_train, y_train)#拟合数据
    print(linreg.coef_)#参数
    print(linreg.intercept_)#截距

![在这里插入图片描述][20200413060603317.png]  
w1*x1+w2*x2+w3\*x3+b=y,和神经网络的图也差不多权重加b来决定y值！！

y_hat = linreg.predict(np.array(x_test))#模型预测
    print(mean_squared_error(y_test, y_hat))#均方误差
    print(mean_absolute_error(y_test, y_hat))#均绝对值误差，度量模型优劣
    print(r2_score(y_test, y_hat))#R方

![在这里插入图片描述][20200413060741924.png]  
只有R方能看出模型的好坏（越接近1越好）

t = np.arange(len(x_test))
    plt.plot(t, y_test, 'r-', linewidth=2, label='Test')
    plt.plot(t, y_hat, 'g-', linewidth=2, label='Predict')
    plt.legend(loc='upper right')
    plt.grid()
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzY3MzE1Ng_size_16_color_FFFFFF_t_70 1]

[20200413060357267.png]: /images/20230528/9ff117b7dfb24eb59a9cfe30626e1b5e.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzY3MzE1Ng_size_16_color_FFFFFF_t_70]: /images/20230528/5ba2c0135fbe4cf893f1b187e0585afd.png
[20200413060603317.png]: /images/20230528/daa606d07db748408b39bf16e49f935f.png
[20200413060741924.png]: /images/20230528/84cd58f81ca74ecfb627e6ee27ca22ae.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzY3MzE1Ng_size_16_color_FFFFFF_t_70 1]: /images/20230528/9e4f17e5d1af43dd9a1cb9e2a32f0a95.png