Models are only as good as their inputs
Swapping algorithms gives diminishing returns; thoughtful features give step changes. The biggest accuracy gains usually come from how you represent the data, not which model you pick.
Techniques that pay off
- Aggregations over time windows (last 7, 30, 90 days).
- Ratios and rates instead of raw counts.
- Encoding categories by their target behaviour.
- Domain features that capture real-world logic.
Avoid the traps
Watch for leakage—features that secretly contain the answer—and keep training and serving features identical. A feature store helps both sides use the same definitions.
