Turning One Categorical Variable into Behavioral Signal
Don't just encode a categorical variable when it actually contains multiple behavioral dimensions.
The Problem
In this classification project, some categorical variables were informative but too coarse in their raw form.
A good example was job_type. Categories like Self employed, Farming and Fishing, Government Dependent, or Formally employed Private don't just describe occupation. They mix several underlying dimensions: how income is generated, how often it arrives, and how savings are managed.
Encoding that variable directly forces the model to recover multiple latent behaviors from one single label.
The Approach
I transformed job_type into multiple behavioral features:
Income source
formal, business, farming, dependent, none
Savings pattern
bank, hand, other
Income frequency
monthly, daily, seasonally, remittance-based, none
Each derived feature captures a different financial mechanism.
Key Takeaway
Design insight: The variable is no longer a flat category — it becomes a behavioral representation layer separating latent mechanisms. Instead of asking the model to learn everything from one raw label, give it a more explicit view of the structure behind the target.