I have a question. Is the best score from GridSearchCV, which corresponds to mean cross-validation score, the right metric to evaluate an algorithm trained with unbalanced data?

CodePudding user response：

GridSearchCV can be used to find appropriate parameter values for your model. For the right metric to evaluate an algorithm trained with unbalanced data, you want to look at the area under the precision-recall curve (PR AUC) or 'average precision' or maybe even a cost-sensitive one (Jason Brownlee has a bunch of blogs on this topic).