#Precision and Recall

Articles about Precision and Recall — exploring patterns, best practices, and real-world implementations in production systems.

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Pratik Dhanave · ·5 min read

Evaluating Models — Accuracy Is the Metric That Lies

The most dangerous number in machine learning is accuracy, because it's the one everyone reaches for and the one that most often misleads. A fraud model that's 99.9% accurate by never catching any fraud is useless and common. Choosing the right evaluation metric — and understanding what each one hides — is the difference between a model that looks good and a model that works. This post is about measuring models honestly.

The most dangerous number in ML is accuracy — a 99.9%-accurate fraud model that catches nothing is common. The confusion matrix, precision vs recall (a business decision), F1/ROC, regression metrics, segment-level evaluation (how bias is caught), and why every offline metric is a proxy (Goodhart's law).

#Machine Learning#Evaluation#Metrics#Precision and Recall#AI Engineering#Fundamentals

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.