In machine learning and data science in general it is very common to deal at some point with imbalanced datasets and class distributions. This is the typical case where the number of observations that belong to one class is significantly lower than those belonging to the other classes.  Actually this happens all the time, in several domains, from finance, to healthcare to social media, just to name a few I have personally worked with.
Think about a bank detecting fraudulent transactions among millions or billions of daily operations, or equivalently in healthcare for the identification of rare disorders.
In genetics but also with clinical lab tests this is a normal scenario, in which, fortunately there are very few patients affected by a disorder and therefore very few cases wrt the large pool of healthy patients (or not affected).
There is no algorithm that can take into account the class distribution or the amount of observations in each class, if it is not explicitly designed to handle such situations.
In this episode I speak about some effective techniques to handle imbalanced datasets, advising the right method, or the most appropriate one to the right dataset or problem.

In this episode I explain how to deal with such common and challenging scenarios.