TL;DRFeature engineering — crafting good inputs — was the old craft; deep learning automates much of it.
In classical ML, much of the work was hand-designing features: ratios, aggregates, encodings that expose the signal to the model. Good features often beat fancy algorithms. Deep learning shifted this — networks learn features from raw data — but feature engineering still matters hugely for tabular data, where trees + good features remain state of the art. Know your domain to craft features.
Key points
Hand-craft inputs that expose the signal
Good features often beat fancier models
Deep learning learns features from raw data
Still vital for tabular ML
Common mistakes
Ignoring features, hoping the model figures it out
Leaking target info into a feature
Assuming deep learning removes the need entirely
Try it: Invent two useful engineered features for predicting loan default.
Example code
<!doctype html><html><head><meta charset="utf-8"></head>
<body style="background:#06040d;color:#e6e0ff;font-family:monospace;padding:20px"><pre>raw: income, debt
engineered: debt/income ratio, payments_missed_last_year
→ exposes the signal to the model</pre></body></html>