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：返回該次預測的系數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數是1，並且可能是負值（因為模型可能會變得更加糟糕）。當一個模型不論輸入何種特征值，其總是輸出期望的y的時候，此時返回0。

sklearn中預測模型的score函數