---
license: other
library_name: sklearn
tags:
- binary-classification
- tabular-classification
- healthcare
- physician-segmentation
---

# 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.