Balancing Act in Datasets of a Machine Learning algorithm

When dealing with imbalanced classes, we may need to do some extra work and planning to make sure that our algorithms give us useful results. In this blog, I examine just two classification techniques to illustrate the issue, but you should know that the problem generalizes. For good reason, supervised classification algorithms -- which use labeled data -- take class distributions into account. However, when we're trying to detect classes that are important, but rare compared to the alternatives, it can be difficult to develop a model that catches them. Here, after diving into the problem with some examples, I outline a few of the tried and true techniques for solving it.