Feature engineering practice

Turning raw fields into features models can use: ratios, bins, embeddings, calendars, text vectorizers—guided by domain sense.

For classical ML, features often beat exotic models. Even deep nets benefit from sane inputs.

Start simple → add domain transforms → check leakage → measure lift on validation → keep a feature dictionary so teammates don’t reinvent.

“Hour of day” and “is_weekend” beat a raw timestamp for many retail models.

List 5 raw fields in a domain you know and 5 derived features you’d try first.

⚠️ Myth: Deep learning removes the need to think about features.

✓ Reality: You still choose representations, windows, and labels.

⚠️ Myth: More features always help.

✓ Reality: Noise and leakage grow—regularize and validate.

• Course 03 features-loss; Course 02 leakage
• Google ML Crash Course: https://developers.google.com/machine-learning/crash-course ↗