sklearn.linear_model.LinearRegression.score
score(self, X, y, sample_weight=None)
Returns the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.
作用：返回该次预测的系数R2
其中R2 =（1-u/v）。
u=((y_true - y_pred) ** 2).sum() v=((y_true - y_true.mean()) ** 2).sum()
其中可能得到的最好的分数是1，并且可能是负值（因为模型可能会变得更加糟糕）。当一个模型不论输入何种特征值，其总是输出期望的y的时候，此时返回0。