线性回归

线性回归，简单讲就是拟合一条直线，意味着可以将输入项分别乘以一些回归系数，再将结果加起来作为输出

训练线性回归算法，就是找到回归系数

#线性回归例子from sklearn import datasetsimport numpy as npdiabetes = datasets.load_diabetes()diabetes_X_train = diabetes.data[:-20]diabetes_X_test  = diabetes.data[-20:]diabetes_y_train = diabetes.target[:-20]diabetes_y_test  = diabetes.target[-20:]from sklearn import linear_modelregr = linear_model.LinearRegression()regr.fit(diabetes_X_train, diabetes_y_train)print(regr.coef_) #输出线性回归的参数# The mean square errorerr = np.mean((regr.predict(diabetes_X_test)-diabetes_y_test)**2)print(err)# Explained variance score: 1 is perfect prediction# and 0 means that there is no linear relationship# between X and y.print(regr.score(diabetes_X_test, diabetes_y_test))