Classification with Imbalanced Data

Building classification models on data that has largely imbalanced classes can be difficult. Using techniques such as oversampling, undersampling, resampling combinations, and custom filtering can improve accuracy. In this article, I'll walk through a few different approaches to deal with data imbalance in classification tasks. To demonstrate various class imbalance techniques, a fictitious dataset of credit card defaults will be used. In our scenario, we are trying to build an explainable classifier that takes two inputs (age and card balance) and predicts whether someone will miss an upcoming payment.