Introduction to MOOSE-FS
=========================

MOOSE-FS is a Python library designed to enhance feature selection by leveraging an ensemble of methods and multi‑objective optimization. It aims to provide robust, reproducible, and tunable pipelines for feature selection in both classification and regression tasks.

Why Use MOOSE-FS?
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MOOSE-FS combines multiple feature selection algorithms to form a stronger decision basis, reducing overfitting risk and improving the stability of selected features.

Features
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- Robustness: Ensemble across selectors and repeats.
- Flexibility: Works with built‑in and custom selectors/mergers/metrics.
- Customizability: Extend via simple base classes.

Getting Started
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To get started with MOOSE-FS, visit the :doc:`installation` section, then see :doc:`usage` for an example pipeline.

