Imbalanced data commonly exist in real world, especially in anomaly-detection tasks. Handling imbalanced data is important to the tasks, otherwise the predictions are biased towards the majority class. RandomOverSampler, SMOTE, and ADASYN are useful oversampling tools to fabricate data for minority classes and make the dataset balanced.
The data for the "Poor" and "Good" classes are much less than the "Standard" class:
The predictions are biased towards the majority class:
Oversampling with RandomOverSampler:
The predictions get a little bit more balanced:
Oversampling with SMOTE:
The predictions get more balanced:
Oversampling with ADASYN:
The predictions get more balanced:
