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。

sklearn中预测模型的score函数

原文：https://www.cnblogs.com/cymwill/p/9239653.html