Evals in Data Science 🔥 Building models is fun… but here’s the real test:
is your model actually any good, or just pretending? 👀
Evaluations, or
evals, are our model’s report card. They tell us:
- For a
spam filter: Do we
catch all spam (recall) without misclassifying grandma’s emails as junk (precision)?
- For
price prediction: How close are our predictions on average (RMSE)?
But evals aren’t just about numbers - they influence trust, fairness, and real-world usefulness of our models.
Discussion prompts:- What’s your go-to evaluation metric and why?
- Seen a model that looked great on paper but flopped in reality?
- Should fairness & usability be considered
first-class evaluation metrics alongside accuracy?
Free book to dive deeper:-
Fairness and Machine Learning: rigorous, practical guide to evaluating models for fairness:
https://fairmlbook.org/Drop your thoughts below ⬇️
