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A complete guide
to classification metrics
in machine learning

For data scientists, ML engineers, product managers, and all practitioners alike.

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.

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