Metadata-Version: 2.4
Name: calm-additive
Version: 0.1.0
Summary: CALM: interpretable by design. Accurate locally additive models for tabular data.
Keywords: additive models,interpretable machine learning,glassbox,tabular,explainable ai
Author-email: Vasilis Gkolemis <ntipakos@gmail.com>
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: effector>=0.6.0
Requires-Dist: numpy
Requires-Dist: scikit-learn
Requires-Dist: xgboost
Requires-Dist: interpret
Requires-Dist: tqdm
Project-URL: homepage, https://givasile.github.io/calm-additive
Project-URL: source, https://github.com/givasile/calm-additive

# CALM: interpretable by design

Accurate locally additive models with conditional feature effects for tabular data.
Python package for the NeurIPS 2026 paper
*Interpretability-by-Design with Accurate Locally Additive Models and Conditional Feature Effects*
([arXiv](https://arxiv.org/abs/2602.16503)).

**Project page:** https://givasile.github.io/calm-additive
**Documentation:** https://givasile.github.io/calm-additive/docs/

```bash
pip install calm-additive
```

```python
from calm_additive import CALMRegressor

model = CALMRegressor().fit(X_train, y_train)
model.predict(X_test)
model.print_summary()
```

Built on [effector](https://github.com/givasile/effector).

