Python for Finance Cookbook

Investigating different approaches to handling imbalanced data

A very common issue when working with classification tasks is that of class imbalance: when one class is highly outnumbered in comparison to the second one (this can also be extended to multi-class). In general, we are talking about imbalance when the ratio of the two classes is not 1:1. In some cases, a delicate imbalance is not that big of a problem, but there are industries/problems in which we can encounter ratios of 100:1, 1000:1, or even worse.

In this recipe, we show an example of a credit card fraud problem, where the fraudulent class is only 0.17% of the entire sample. In such cases, gathering more data (especially of the fraudulent class) might simply not be feasible, ...