scikit-learn-线性回归（最小二乘法）

 

from sklearn import linear_model
reg = linear_model.LinearRegression()
reg.fit ([[0, 0], [1, 1], [2, 2]], [0, 1, 2])  训练函数
—————————>LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
reg.coef_   斜率
—————————>array([ 0.5,  0.5])
print(reg.intercept_) 截距

划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)

#模型拟合测试集   均方差MSE  均根方差RMSE
y_pred = linreg.predict(X_test)
from sklearn import metrics
# 用scikit-learn计算MSE
print "MSE:",metrics.mean_squared_error(y_test, y_pred)
# 用scikit-learn计算RMSE
print "RMSE:",np.sqrt(metrics.mean_squared_error(y_test, y_pred))
 

https://blog.youkuaiyun.com/deramer1/article/details/79055281