Examples#

Welcome to the Ex-Fuzzy examples gallery! Here you’ll find practical examples demonstrating how to use Ex-Fuzzy for various machine learning tasks.

Classification Examples

Learn fuzzy classification with practical examples using the Iris dataset and other scenarios.

Classification Examples
Regression Examples

Train interpretable regressors with crisp or fuzzy consequents on CPU or GPU.

Regression Examples
FERL

Learn a fuzzy rule tree with native belief, plausibility, ignorance, and prediction sets.

FERL Example

Working Examples#

The repository ships seven executed notebooks in Demos/. They render with their outputs on GitHub and each runs in well under a minute; the Titanic and California housing notebooks download their data through scikit-learn on the first run.

Demo notebooks#

Notebook

What it shows

01 Getting started

Fit, score, read the rules, probabilities, per-sample explanations, partition plots.

02 Scikit-learn integration

Titanic data with categorical columns and missing values: a pipeline with imputation, cross-validation, grid search.

03 Rules and partitions

Fuzzy sets and rules by hand, fixed versus optimised partitions, Type-2 sets, validation, inference modes, LaTeX export, saving and loading.

04 Controlling the search

Budget and early stopping, custom objectives, checkpoints, mined candidate rules, all classifiers compared.

05 Regression

Crisp and Mamdani consequents on California housing, then inference by hand.

06 Uncertainty

Conformal prediction sets with coverage evaluation, next to FERL and DeepFERL evidential outputs.

07 Robustness

Pattern stability over repeated fits, permutation and bootstrap validation.

The EvoX backend comparison is a script, Demos/evox_backend_demo.py, since it needs the optional backend. To run the notebooks yourself, open them with Jupyter after installing the package, or launch them in Binder.

Example Categories#

Beginner Examples

  • Basic iris classification

  • Trainable Type-1 fuzzy regression

  • Simple pattern analysis

  • Visualization basics

Intermediate Examples

  • Custom fuzzy sets

  • Multi-objective optimization

  • Performance comparison

Advanced Examples

  • Large-scale datasets

  • Real-time inference

  • Integration with other ML libraries

Contributing Examples#

We welcome contributions of new examples! If you have an interesting use case or application of Ex-Fuzzy:

  1. Create a clear, well-documented notebook

  2. Include explanations and visualizations

  3. Test with sample data

  4. Submit a pull request

See our Contributing guide for more details.