Why Your Logistic Regression Model Overfits African Financial Data (and How to Fix It with Regularization)
You don't need cross-validation to spot overfitting. A simple 70-30 train-test split with accuracy tracking is often enough for preliminary checks.
For African financial data, I typically start with C=0.1 instead of the default 1.0 — our smaller datasets need stronger regularization to prevent overfitting.
Use Elastic Net when you have: • Highly correlated features (common in African financial data) • Many irrelevant features • Need both coefficient shrinkage AND sparsity
I've seen regularization outperform random forests and gradient boosting on African financial data <100k records. The tree-based methods overfit even more aggressively on small, noisy datasets.
The most common mistake I see? People start with C=1.0 then wonder why their small dataset is overfitting. African financial data almost always needs stronger regularization than typical Western datasets.