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model = GridSearch(...)
model.fit(..., scoring='roc_auc')
I do have validation and test sets, the former of which is passed into model.fit(..). To get both validation and test ROC scores, I call model.score() on both my validation and test sets.
Since I would like to keep track of the ROC returned by each set of hyperparameters, I am also saving the output from model.get_param_scores(). From my understanding, the first ROC from model.get_param_scores() should be identical to my validation ROC obtained from model.score() (since they are sorted in descending order). However, the values are completely different.
My question is: Is model.score() actually using the specified metric (i.e. roc_auc) or is it only computing accuracy?
Thank you!
The text was updated successfully, but these errors were encountered:
I am calling:
model = GridSearch(...)
model.fit(..., scoring='roc_auc')
I do have validation and test sets, the former of which is passed into model.fit(..). To get both validation and test ROC scores, I call model.score() on both my validation and test sets.
Since I would like to keep track of the ROC returned by each set of hyperparameters, I am also saving the output from model.get_param_scores(). From my understanding, the first ROC from model.get_param_scores() should be identical to my validation ROC obtained from model.score() (since they are sorted in descending order). However, the values are completely different.
My question is: Is model.score() actually using the specified metric (i.e. roc_auc) or is it only computing accuracy?
Thank you!
The text was updated successfully, but these errors were encountered: