How to evaluate the quality of a classification model? In this guide, we break down different machine learning metrics for binary and multi-class problems.
What you will learn in this guide:
- How to calculate the key classification metrics, including accuracy, precision, recall, F1 score, and ROC AUC.
- The pros and cons of each metric, how they behave in corner cases, and when some metrics are more suitable.
- Practical tips for using classification metrics in production settings and ML monitoring.
Here is what makes this guide different:
- Explaining the intuition behind the metrics. We link to the formulas when needed but focus on simple explanations anyone can understand.
- Illustrated guide. We added a lot of images, making it easy to follow along and visualize how each metric works.
- Real-world examples. Rather than abstract scenarios, we use relatable business cases that you might encounter in your work.
There is no need to read the guide cover-to-cover: each article is self-contained, and you can read it individually.
