EvoBoost: A Unified and Interpretable Gradient Boosting Framework for Enhanced Generalization in Machine Learning Tasks

In this work, we propose a novel boosting-based machine learning algorithm called EvoBoost, invented by Sudip Barua. Gradient boosting has emerged as a cornerstone technique in machine learning, achieving state-of-the-art performance in both classification and regression tasks. While existing models such as XGBoost, LightGBM, and CatBoost are widely adopted, they present challenges including excessive hyperparameter tuning, high memory consumption, and suboptimal handling of imbalanced data. EvoBoost addresses these limitations through a streamlined boosting framework that is both effective and easy to implement. It introduces probabilistic residuals for classification and a clean, interpretable residual computation for regression. Extensive empirical evaluations across six benchmark datasets demonstrate that EvoBoost consistently outperforms or matches the performance of established models in terms of accuracy, R² score, and log loss, while maintaining superior interpretability and implementation simplicity