semvec/pypi-packages huggingface dataset:
fseval 3.1.0
Benchmarking framework for Feature Selection and Feature Ranking algorithms 🚀
License: MIT
Requires Python: >= 3.7

# fseval

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Benchmarking framework for Feature Selection and Feature Ranking algorithms 🚀

## Demo
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Bsuxxuw0-mEsYRSnNbmvD_wNUAkOPiQa?usp=sharing)

## Install

1. Installation through [PyPi](https://pypi.org/project/fseval/)
    <sup><sub>
    ⭐️ RECOMMENDED OPTION
    </sub></sup>

    ```shell
    pip install fseval
    ```


2. Installation from source 

    ```shell
    git clone https://github.com/dunnkers/fseval.git
    cd fseval
    pip install -r requirements.txt
    pip install .
    ```

You can now import fseval `import fseval` in your Python code, or use the `fseval` command in your terminal. For an example, run `fseval --help`. For more information, see the documentation link below ⌄. 

## Documentation

[![docs preview](./website/static/img/docs-preview.png)](https://dunnkers.com/fseval)

See the [documentation](https://dunnkers.com/fseval).

## About
Built at the University of Groningen. It stemmed from a previous project that proposed the feature selection algorithm called FeatBoost (see full citation below). The open source Python code of FeatBoost is available in https://github.com/amjams/FeatBoost.


A. Alsahaf, N. Petkov, V. Shenoy, G. Azzopardi, "A framework for feature selection through boosting", Expert Systems with Applications,
Volume 187, 2022, 115895, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2021.115895.


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<p align="center">2022 — <a href="https://dunnkers.com/">Jeroen Overschie</a></p>