xgb-deepmodel

What problem does it solve?

This Skill addresses how to improve financial modeling performance when different customer segments behave differently, by training separate XGBoost sub-models per segment and then fusing them via Stacking to reduce global bias and better capture segment-specific patterns.

Core Features & Use Cases

• Segmented XGBoost sub-model training: Train multiple XGBoost models using mutually exclusive segment conditions, with optional per-segment feature sets and positive class weighting.
• OOF Stacking fusion: Generate out-of-fold (OOF) predictions from each sub-model and train a meta-learner (conservative parameters) to combine them safely.
• Ensemble vs baseline comparison report: Compare the best sub-model vs Stacking ensemble against an XGBoost single-model baseline using Train/Val/OOT AUC/KS and gap-based decision guidance.
• Use cases: segment modeling, customer stratification, sub-model fusion, and advanced ensemble learning scenarios (e.g., banking/insurance/securities risk scoring).

Quick Start

Train segment sub-models, fuse them with stacking, and generate an ensemble vs baseline comparison report for your labeled dataset using the three scripts in order: sub_trainer → stacker → comparator.