线性回归算法

悠悠 2022-01-23 07:27 305阅读 0赞

以sklearn.datasets模块中的经典数据load\_boston（波士顿房价）为例，实现线性回归算法

代码：

from sklearn.datasets import load_boston  # 经典数据
    from sklearn.linear_model import LinearRegression # 线性模块，回归线性模型
    from sklearn.model_selection import train_test_split # 训练集与测试集分割模型
    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    
    # 记载数据
    boston = load_boston()
    print(type(boston))
    print(list(boston))
    x = boston['data']  # 数据的特征值
    y = boston['target'] # 标签值
    names = boston['feature_names'] # 特征名称
    print(names)
    print(type(names))
    
    
    df = pd.DataFrame(x,columns=names,index=range(len(y)))
    pf = pd.DataFrame(y,columns=['label'],index=range(len(y)))
    print(df.shape)
    print(pd.concat((df, pf), axis=1).shape)
    data = pd.concat((df, pf), axis=1)
    # data.to_excel('boston.xls')
    # 分割数据
    x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2)
    # 构建回归模型
    clf = LinearRegression().fit(x_train,y_train)
    print('截距是',clf.intercept_)
    print('回归系数是',clf.coef_)
    
    # 预测
    y_pred = clf.predict(x_test)
    print('预测值是',y_pred[:20])
    print('实际值是',y_test[:20])
    # 将预测标签与真实标签拼接到一起，称为一个达标
    print(np.concatenate((y_pred.reshape(-1,1), y_test.reshape(-1,1)),axis=1).shape)
    result = np.concatenate((y_pred.reshape(-1,1), y_test.reshape(-1,1)),axis=1)
    print(result)
    
    x = range(1,103)
    plt.figure()
    plt.rcParams['font.sans-serif'] = 'SimHei' # 仿宋字体
    plt.rcParams['axes.unicode_minus'] = False # 设置正常显示符号
    plt.plot(x,result[:,0],marker='*')
    plt.plot(x,result[:,1],marker='o')
    plt.legend(['房价预测值','房价真实值'])
    plt.show()

实现效果：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDIzOTU0MQ_size_16_color_FFFFFF_t_70][]

”

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDIzOTU0MQ_size_16_color_FFFFFF_t_70]: /images/20220123/f23a7b461dee4c10b0ca373776f973b8.png