| --- |
| license: mit |
| library_name: generic |
| pipeline_tag: tabular-classification |
| tags: |
| - fairness |
| - calibration |
| - multicalibration |
| - gradient-boosting |
| - gbdt |
| - decision-trees |
| - trustworthy-ai |
| - tabular |
| - risk-assessment |
| - risk |
| arxiv: "2509.19884" |
| model-index: |
| - name: MCGrad |
| results: [] |
| --- |
| |
| # MCGrad: Multicalibration at Web Scale |
| **Production-ready multicalibration for machine learning.** *Developed by Meta. Accepted at KDD 2026.* |
|
|
| **Paper:** [arXiv:2509.19884](https://arxiv.org/abs/2509.19884) |
| **Official Code:** [github.com/facebookincubator/MCGrad](https://github.com/facebookincubator/MCGrad) |
| **Documentation:** [mcgrad.dev](https://mcgrad.dev) |
|
|
| ## Overview |
| MCGrad is a library for production-ready multicalibration. It ensures your ML model predictions are well-calibrated not just globally, but across virtually any segment defined by your features. |
|
|
| ## Installation |
| ```bash |
| pip install mcgrad |
| ``` |
| ## Getting started |
| See [mcgrad.dev](https://mcgrad.dev) |
|
|
|
|
