Tour of Evaluation Metrics for Imbalanced Classification

A classifier is only as good as the metric used to evaluate it. If you choose the wrong metric to evaluate your models, you are likely to choose a poor model, or in the worst case, be misled about the expected performance of your model. Choosing an appropriate metric is challenging generally in applied machine learning, but is particularly difficult for imbalanced classification problems. Firstly, because most of the standard metrics that are widely used assume a balanced class distribution, and because typically not all classes, and therefore, not all prediction errors, are equal for imbalanced classification. In this tutorial, you will discover metrics that you can use for imbalanced classification. Tour of Evaluation Metrics for Imbalanced Classification Photo by Travis Wise, some rights reserved.