Measuring Agreement with Cohen’s Kappa Statistic

This lesser-known metric can help you better evaluate how models perform on imbalanced data

A lot of the most intriguing — to me — use cases for classifications are to identify outliers. The outlier may be a spam message in your inbox, a diagnosis of an extremely rare disease, or an equity portfolio with extraordinary returns. Due to these instances being outliers, it is hard to gather enough data to train a model on how to spot them. Some people dedicate their entire careers to creating…