`OIKAN (v0.0.3)` Neuro-Symbolic ML for Scientific Discovery - Release 🚀

👋 Hello, GitHub Community! I am glad to share with you the release of the OIKAN project, which aims to create interpretable machine learning models using neural networks and symbolic regression.

OIKAN (v0.0.3) is a neuro-symbolic machine learning framework inspired by Kolmogorov-Arnold Representation Theory (KART), designed to deliver accurate, interpretable, and efficient models for tabular data. By integrating the strengths of neural networks and symbolic regression, OIKAN produces human-readable mathematical formulas that approximate complex data relationships while maintaining high predictive performance. Unlike traditional approaches, which suffer from high computational complexity, OIKAN employs a streamlined approach that balances interpretability, efficiency, and accuracy.

Key Features:

• 🧠 Neuro-Symbolic ML: Combines neural network learning with symbolic mathematics;
• 📊 Automatic Formula Extraction: Generates human-readable mathematical expressions;
• 🎯 Scikit-learn Compatible: Familiar .fit() and .predict() interface;
• 🔬 Research-Focused: Designed for academic exploration and experimentation;
• 📈 Multi-Task: Supports both regression and classification problems;

Links to the project:

• GitHub: https://github.com/silvermete0r/oikan
• PyPI: https://pypi.org/project/oikan/

High-level Architecture:

Get Started Template Notebook: https://www.kaggle.com/code/armanzhalgasbayev/oikan-v0-0-3-get-started-template-notebook

v0.0.2

OIKAN is a neuro-symbolic ML framework that combines modern neural networks with classical Kolmogorov-Arnold representation theory. It provides interpretable machine learning solutions through automatic extraction of symbolic mathematical formulas from trained models.

Important Disclaimer: OIKAN is an experimental research project. It is not intended for production use or real-world applications. This framework is designed for research purposes, experimentation, and academic exploration of neuro-symbolic machine learning concepts.

Key Features

• 🧠 Neuro-Symbolic ML: Combines neural network learning with symbolic mathematics
• 📊 Automatic Formula Extraction: Generates human-readable mathematical expressions
• 🎯 Scikit-learn Compatible: Familiar .fit() and .predict() interface
• 🔬 Research-Focused: Designed for academic exploration and experimentation
• 📈 Multi-Task: Supports both regression and classification problems