pfizer-project-team/binary-segA-vs-segBC

Binary SEG_A vs SEG_B/C Classifier

This repository contains the selected binary classifier for the first stage of a hierarchical physician segmentation strategy.

Task

Binary classification:

• 0: SEG_A
• 1: SEG_B/C

The model predicts whether a physician belongs to SEG_A or should be routed to the second-stage SEG_B vs SEG_C classifier.

Selected Model

Best model: HistGradientBoosting
Decision threshold for SEG_B/C: 0.45

Files

• best_binary_segA_vs_segBC.joblib: trained model
• model_metadata.json: model configuration and selected threshold
• binary_model_threshold_comparison_validation.csv: validation threshold comparison
• test_predictions_binary_segA_vs_segBC_with_hcp_id.csv: test-set predictions with HCP ID

Notes

The model uses flattened temporal tensors as input. Each physician is represented by weekly behavior across multiple features.

The prediction probability prob_SEG_BC can be used to decide whether a physician should be classified as SEG_A or passed to the next B/C decision model.