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I trained a model using Random Forests with 500 estimators. I have an imbalanced dateset and the scoring metric is f1 score. The cross validation score on train data using F1 metric is 0.95, roc-auc metric is 0.97. The f1 score on test data is 0.75 and roc-auc score on test data is 0.92. I think that the low f1 score on test data is because of class imbalance. Is my model over-fitting?



Read more here: https://stackoverflow.com/questions/64399049/over-fitting-or-not-evaluation-metric-is-f1-score

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