Choosing the Right Metric for Evaluating Machine Learning Models — Part 1

First part of the series focussing on Regression Metrics

In the world of postmodernism, Relativism has been, in its various guises, both one of the most popular and most reviled philosophical doctrines. According to Relativism, there is no universal and objective truth; rather each point of view has its own truth. You must be wondering why I am discussing it and how it is even related to Data Science.

Well, in this post, I will be discussing the usefulness of each error metric depending on the objective and the problem we are trying to solve. When someone tells you that “USA is the best country”, the first question that you should ask is on what basis is this statement being made. Are we judging each country on the basis of their economic status, or their health facilities etc.? Similarly each machine learning model is trying to solve a problem with a different objective using a different dataset and hence, it is important to understand the context before choosing a metric.

Most Useful Metrics