Welcome to EvoRBF’s documentation!¶
EvoRBF: Evolving Radial Basis Function Network by Intelligent Nature-inspired Algorithms
EvoRBF (Evolving Radial Basis Function Network) is a Python library that implements a framework for training Radial Basis Function (RBF) networks using Intelligence Nature-inspired Algorithms (INAs). It provides a comparable alternative to the traditional RBF network and is compatible with the Scikit-Learn library. With EvoRBF, you can perform searches and hyperparameter tuning using the functionalities provided by the Scikit-Learn library.
Free software: GNU General Public License (GPL) V3 license
Provided Estimator: RbfRegressor, RbfClassifier, InaRbfRegressor, InaRbfClassifier, InaRbfTuner
Total InaRBf models: > 400 Models
Supported performance metrics: >= 67 (47 regressions and 20 classifications)
Supported loss functions: >= 61 (45 regressions and 16 classifications)
Documentation: https://evorbf.readthedocs.io/en/latest/
Python versions: >= 3.8.x
Dependencies: numpy, scipy, scikit-learn, pandas, mealpy, permetrics