thiefall/Thiemokho-Code-essay-AIMS-Senegal

# 🧬 Symbolic Regression: A Methodological Framework for Interpretable Learning > *Research thesis exploring symbolic regression as an interpretable alternative to classical machine learning methods* --- ## 📚 Table of Contents - 📖 About - 🎯 Objectives - 🔬 Methodology - 📊 Key Results - 🚀 Installation - 🔍 Applications - 📈 Comparisons - 📝 Citation - 👥 Author - 📄 License --- ## 📖 About This repository contains the code and experiments associated with my research thesis on **Symbolic Regression (SR)** conducted at the **African Institute for Mathematical Sciences (AIMS) - Senegal**. ### What is Symbolic Regression? Symbolic regression is a machine learning technique that, unlike "black box" methods (neural networks, random forests), generates **explicit mathematical expressions** relating input variables to the target. **Example:** ```python # Instead of an opaque model, SR produces: f(x, y) = 2.5*x² + sin(y) - 3.1 # A readable, interpretable, and verifiable formula! ``` ### 🌟 Why It Matters - ✅ **Interpretability**: Explicit formulas understandable by domain experts - ✅ **Law Discovery**: Reveals hidden physical or biological relationships - ✅ **Generalization**: Better extrapolation beyond the training domain - ✅ **Data Efficiency**: Performs well even with few samples - ✅ **Scientific Validation**: Results can be theoretically verified --- ## 🎯 Objectives This thesis aims to: 1. **Analyze the mathematical and algorithmic foundations** of symbolic regression 2. **Compare SR with classical ML methods** (SVR, Random Forest, Neural Networks) 3. **Evaluate performance on synthetic and real data** (diabetes, finance, physics) 4. **Demonstrate scientific discovery capability** through rediscovery of physical laws 5. **Provide a complete methodological framework** for practical use of SR --- ## 🔬 Methodology ### Three Algorithmic Paradigms Explored #### 1️⃣ **Genetic Programming (GP)** Population of expressions evolving through natural selection, cross …