Precision, Recall, F-1 score- Must Know Before Your Next Project!
Last Updated on July 25, 2023 by Editorial Team
Author(s): Anmol Tomar
Originally published on Towards AI.
The intuition behind the classification evaluation metrics

This member-only story is on us. Upgrade to access all of Medium.
Pic Credit: Unsplash
Imagine you are building a fraud detection model to identify the fraudulent transactions done using a credit card. You look into the data and find that the majority of the transactions are non-fraudulent(99%), and only 1% of the transactions are fraudulent. You simply tagged every transaction as non-fraudulent and got an accuracy of 99%, WOW!
But, if you go to the client to deploy such a model, they will call you a FRAUD U+1F601.
The above scenario is an example of an imbalanced classification problem, and the “accuracy” is… Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI Academy
We Build Enterprise-Grade AI. We'll Teach You to Master It Too.
15 engineers. 100,000+ students. Towards AI Academy teaches what actually survives production.
Start free — no commitment:
→ 6-Day Agentic AI Engineering Email Guide — one practical lesson per day
→ Agents Architecture Cheatsheet — 3 years of architecture decisions in 6 pages
Our courses:
→ AI Engineering Certification — 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course out there.
→ Agent Engineering Course — Hands on with production agent architectures, memory, routing, and eval frameworks — built from real enterprise engagements.
→ AI for Work — Understand, evaluate, and apply AI for complex work tasks.
Note: Article content contains the views of the contributing authors and not Towards AI.