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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.

Explore topics

Are you looking for an open-source tool to build an ML monitoring dashboard from scratch? Or, have you already started using the Evidently Reports for ML monitoring but now look at how to turn them into a web app?

One option is to use Evidently together with Streamlit, an open-source Python tool for creating shareable web apps. In this tutorial, you will go through an example of creating and customizing an ML monitoring dashboard using the two open-source libraries.


In this tutorial, we will explore issues affecting the performance of NLP models in production.

We will work with a drug review dataset and go through the following steps:
- Train a simple review classification model, and evaluate its quality on a validation dataset;
- Imitate data quality issues and test their impact on the model accuracy;
- Explore how to detect and debug model quality decay.  


How big of drift is DRIFT? Should I care if only 10% of my features drifted? Should I look at drift week-by-week or month-by-month?

You can look at historical drift in data to understand how your data changes and choose the monitoring thresholds. Here is an example with Evidently, Plotly, Mlflow, and some Python code.


How to decide if any of the trained models is good enough for production use? How to evaluate and compare your models beyond the standard performance checks?

In this tutorial, we will walk through an example of how to assess your model in more detail.


What can go wrong with ML model in production, and how to keep track? Let's walk through an example.

It is a story of how we trained a model, simulated production use, and analyzed its gradual decay.


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