Metadata-Version: 2.1
Name: itershap
Version: 0.1.1
Summary: Iterative feature selection method using SHAP values
Home-page: https://github.com/FrankvanMourik/IterSHAP
License: MIT
Keywords: SHAP,Feature Selection,Explainable AI,XAI,Small datasets
Author: Frank van Mourik
Author-email: f.g.vanmourik@student.utwente.nl
Requires-Python: >=3.10,<3.11
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: shap
Requires-Dist: xgboost
Project-URL: Repository, https://github.com/FrankvanMourik/IterSHAP
Description-Content-Type: text/markdown

# IterSHAP: Iterative feature selection using SHAP values
Author: Frank van Mourik, University of Twente

## Installation
Install via [pip](https://pypi.org/project/itershap/) using: ```pip install itershap``` (requires Python version >=3.10,<3.11).

## Usage
```py
from itershap import IterSHAP

X, y = get_data() # Replace with data location

fs = IterSHAP() # Create a IterSHAP feature selection object
fs.fit(X, y) # Execute IterSHAP on input data
X_transformed = fs.transform(X) # Only keep the via IterSHAP selected features
```

## Benefits
* Performs well on small high-dimensional datasets
* Guarantees to return a feature subset
* Model-agnostic (limited by [shap](https://github.com/slundberg/shap) supported models)
* Validated on synthesised data
* Benchmarked on [DEAP dataset](https://www.eecs.qmul.ac.uk/mmv/datasets/deap/)

## License
Available under the MIT license, which can be found [here](LICENSE.txt)

