Feature Engineering
Models only see the columns you hand them. Feature engineering is reshaping raw data so the signal becomes visible — ratios, interactions, date parts, rolling aggregates — and on tabular problems it beats model choice as the biggest accuracy lever.
01.The Problem: The Model Sees Only Through Your Keyholes
Two loan applicants walk into a bank.
- Amala: debt 50,000, income 60,000.
- Raj: debt 50,000, income 5,000,000.
Same debt. Very different risk — Amala is stretched thin; Raj barely notices the loan.
You feed the model two columns, debt and income, and a simple model tries to learn from them. It sees two numbers that each mean something alone, and must work very hard to discover that what actually matters is their ratio.
The signal exists in your data — but is it in a shape the model can see?
Every model looks at the world through the holes you drill. A linear model adds up columns one at a time and cannot multiply them on its own. A decision tree (Topic 56) can only split on single columns per node, so a combination that only matters jointly must be surfaced as its own feature.
If the useful pattern lives between the columns, no cleverness inside the model will find it cheaply. You have to build the column that contains it.
That building work is feature engineering — and on tabular problems it consistently beats model architecture as the driver of performance: a well-engineered ratio or interaction can add more accuracy than swapping linear regression for a deep net.
Raw Columns to Engineered Features
Raw Columns to Engineered Features
Feature engineering derives new, more informative columns through transforms, interactions, aggregations and ratios so models can learn the underlying signal.
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