Imbalanced data commonly exist in real world, especially in anamoly-detection tasks. Handling imbalanced data is important to the tasks, otherwise the predictions are biased towards the majority class. BalancedRandomForestClassifier can deal with the imbalanced data without knowing any novel techniques like SMOTE.
Classification report for RandomForestClassifier:
Classification report for BalancedRandomForestClassifier:
