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 ⬇️