Investigating different approaches to handling imbalanced data
A very common issue when working with classification tasks is that of class imbalance, that is, when one class is highly outnumbered in comparison to the second one (this can also be extended to multi-class cases). In general, we are dealing with 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 more extreme.
Dealing with highly imbalanced classes can result in the poor performance of ML models. That is because most of the algorithms implicitly assume balanced distribution of classes. They do so by aiming to minimize the overall prediction error, to which the minority class by definition contributes very little. As a result, classifiers trained on imbalanced data are biased toward the majority class.
One of the potential solutions to dealing with class...
