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Precision, Recall, and F1 Score is a easy coding interview question covering machine learning, math — the kind asked at FAANG (Facebook/Meta, Apple, Amazon, Netflix, Google) and AI-first companies like Anthropic, OpenAI, and Nvidia.
Given binary predictions and ground-truth labels, compute precision, recall, and F1 score from scratch, handling the zero-denominator edge cases. These are the evaluation metrics quoted for every classifier, retrieval system, and ML-powered feature.
Solution walkthrough on Pro
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What's inside this guide
Longer written walkthroughs of the ideas behind it.
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A full interview loop is more than one round. These are the others, each with what is on the site for it.