# FPDE

## Docs

- [API reference](https://fpde-80-mintlify-f19c7fde.mintlify.app/api-reference.md): Reference for FPDE engine, prototype helpers, explanation functions, metrics, plotting helpers, and result objects exported from fpde.
- [Examples](https://fpde-80-mintlify-f19c7fde.mintlify.app/examples.md): FPDE usage patterns for single samples, batches, lambda selection, grid search, prototype helpers, and matplotlib visualization.
- [Introduction](https://fpde-80-mintlify-f19c7fde.mintlify.app/index.md): FPDE is a Python package for prototype-contrast feature attribution that explains scikit-learn classifiers with per-feature contributions.
- [Method overview](https://fpde-80-mintlify-f19c7fde.mintlify.app/method-overview.md): Understand prototypes, target-rival contrasts, and FPDE variants
- [Quickstart](https://fpde-80-mintlify-f19c7fde.mintlify.app/quickstart.md): Install FPDE from PyPI, fit an engine on training data, and explain one scikit-learn classifier prediction with Hyb-FPDE attributions.
- [Reproducibility checklist](https://fpde-80-mintlify-f19c7fde.mintlify.app/reproducibility.md): Record the environment, data, model, and FPDE settings behind an experiment
- [Validation and selection](https://fpde-80-mintlify-f19c7fde.mintlify.app/validation.md): Select Hyb-FPDE settings with grid search and perturbation curves

## Optional

- [GitHub](https://github.com/fpde-xai/fpde)
- [PyPI](https://pypi.org/project/fpde/)