#StackBounty: #random-forest #scikit-learn RandomForestClassifier OOB scoring method

Bounty: 50

Is the random forest implementation in scikit-learn using mean accuracy as its scoring method to estimate generalization error with out-of-bag samples? This is not mentioned in the documentation, but the score() method reports the mean accuracy.

I have a highly unbalanced dataset, and I am using AUC of ROC as my scoring metric in the grid search. Is there a way to tell the classifier to use the same scoring method on the OOB samples as well?


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