The curse of imbalanced data refers to machine learning models trained to predict outcomes which are majority, and neglecting the minority. This is a common problem in fraud detection, cancer classification, etc. In those examples, although the occurrence of the positive outcome is rare, it is highly crucial that the machine learning model is able …

Continue reading Balanced Bootstrapping with Random Forest for Imbalanced Data Sets