线性回归正规方程sklearn实现

# -*- coding: utf-8 -*- from numpy import * import pandas as pd ###线性回归#### # 读取数据 data = pd.read_csv('Advertising.csv', index_col=0) #前五条数据 data.head() #后五条数据 data.tail() # 画散点图 import seaborn as sns import matplotlib ''' % matplotlib inline ''' #画散点图 sns.pairplot(data, x_vars=['TV', 'radio', 'newspaper'], y_vars='sales', size=7, aspect=0.8) sns.pairplot(data, x_vars=['TV', 'radio', 'newspaper'], y_vars='sales', size=7, aspect=0.8, kind='reg') sns.pairplot(data,x_vars=['TV', 'radio', 'newspaper'], y_vars='sales') # 计算相关系数矩阵 data.corr() # 构建X、Y数据集 X = data[['TV', 'radio', 'newspaper']] X.head() y = data['sales'] y.head() #通过正规方程求解最小二乘 ##直接根据系数矩阵公式计算 def standRegres(xArr, yArr): xMat = mat(xArr); yMat = mat(yArr).T xTx = xMat.T * xMat if linalg.det(xTx) == 0.0: print("This matrix is singular, cannot do inverse") #奇异矩阵不能求逆 return ws = xTx.I * (xMat.T * yMat) return ws # 求解回归方程系数 X2 = X X2['intercept'] = [1] * 200 standRegres(X2, y) ##利用sklearn求解 from sklearn.linear_model import LinearRegression linreg = LinearRegression() linreg.fit(X, y) #打印偏置 print(linreg.intercept_) #打印相关系数 print(linreg.coef_) print(zip(['TV', 'radio', 'newspaper'], linreg.coef_)) ##测试集和训练集的构建 交叉验证 from sklearn.cross_validation import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1) linreg.fit(X_train, y_train) # 结果 print(linreg.intercept_) print(linreg.coef_) print(zip(['TV', 'Radio', 'Newspaper'], linreg.coef_)) # 预测 y_pred = linreg.predict(X_test) # 误差评估 from sklearn import metrics # calculate MAE using scikit-learn print("MAE:", metrics.mean_absolute_error(y_test, y_pred)) # calculate MSE using scikit-learn print("MSE:", metrics.mean_squared_error(y_test, y_pred)) # calculate RMSE using scikit-learn print("RMSE:", sqrt(metrics.mean_squared_error(y_test, y_pred))) ##去除newspaper特征 模型比较 feature_cols = ['TV', 'radio'] X = data[feature_cols] y = data.sales X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1) linreg.fit(X_train, y_train) y_pred = linreg.predict(X_test) # calculate MAE using scikit-learn print("MAE:", metrics.mean_absolute_error(y_test, y_pred)) # calculate MSE using scikit-learn print("MSE:", metrics.mean_squared_error(y_test, y_pred)) # calculate RMSE using scikit-learn print("RMSE:", sqrt(metrics.mean_squared_error(y_test, y_pred)))