Neura saysFeature 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.
Power-ups you unlock
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
The Overfit Ogre attacks — common mistakes
Ignoring features, hoping the model figures it out
Leaking target info into a feature
Assuming deep learning removes the need entirely
Boss battleInvent 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>