Hello, I have 2 questions regarding cross_val_score. 1. Do the scores returned by cross_val_score correspond to only the test set or the whole data set (training and test sets)? I tried to look at the source code, and it looks like it returns the score of only the test set (line 145: "return_train_score=False") - I am not sure if I am reading the codes properly, though.. https://github.com/scikit-learn/scikit-learn/blob/14031f6/sklearn/model_ selection/_validation.py#L36 I came across the paper below and the authors use the score of the whole dataset when the author performs repeated nested loop, grid search cv, etc.. e.g. see algorithm 1 (line 1c) and 2 (line 2d) on page 3. https://jcheminf.springeropen.com/articles/10.1186/1758-2946-6-10 I wonder what's the pros and cons of using the accuracy score of the whole dataset vs just the test set.. any thoughts?
2. On line 283 of the cross_val_score source code, there is a function _score. However, I can't find where this function is called. Could you let me know where this function is called? Thank you very much! Raga
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