segment-modeling

To train separate sub-models for customer segments, you apply a segmentation strategy to split users and train an independent XGBoost model for each segment. This approach optimizes binary classification by tailoring models to specific groups rather than using a single global model.

The best way to discover meaningful customer segments is exploring rule-based, clustering-based, and decision-tree-based hypotheses. By running multiple rounds of segment generation and measuring performance with AUC or KS, you keep effective segments and discard unstable ones.

You aggregate predictions from segment-specific XGBoost models using either routing or stacking style aggregation. Routing directs inputs through their matched segment model, while stacking combines outputs from all segment models to produce the final binary classification prediction.

You check the stability of customer segmentation schemes by enforcing minimum segment coverage and penalizing distribution drift via PSI. Applying these stability constraints ensures your segmentation strategy remains robust and prevents fragile sub-models.

Yes, this segmentation approach works with your existing data splits. It requires loading your dataset and producing train, validation, and OOT splits to evaluate segment-specific XGBoost models, ensuring out-of-time testing validates the selected segmentation strategy.

You should use multiple sub-models instead of one single model to improve binary classification performance for distinct groups. Training segment-specific models captures behavioral differences across customer segments, achieving better AUC and KS than a generalized single model.