This article was written by Jason Brownlee. Jason is the editor-in-chief at MachineLearningMastery.com.He has a Masters and PhD in Artificial Intelligence, has published books on Machine Learning and has written operational code that is running in production. After you make predictions, you need to know if they are any good. There are standard measures that we can use to summarize how good a set of predictions actually are. Knowing how good a set of predictions is, allows you to make estimates about how good a given machine learning model of your problem, In this tutorial, you will discover how to implement four standard prediction evaluation metrics from scratch in Python.