3 papers
cs.LG2026
Evaluating Supervised Machine Learning Models: Principles, Pitfalls, and Metric Selection
Xuanyan Liu, Ignacio Cabrera Martin, Marcello Trovati +2
The evaluation of supervised machine learning models is a critical stage in the development of reliable predictive systems. Despite the widespread availability of machine learning…
cs.LG2025
Utilizing Large Language Models for Machine Learning Explainability
Alexandros Vassiliades, Nikolaos Polatidis, Stamatios Samaras +5
This study explores the explainability capabilities of large language models (LLMs), when employed to autonomously generate machine learning (ML) solutions. We examine two classifi…
cs.LG2025
Evolving Machine Learning in Non-Stationary Environments: A Unified Survey of Drift, Forgetting, and Adaptation
Ignacio Cabrera Martin, Subhaditya Mukherjee, Almas Baimagambetov +2
In an era defined by rapid data evolution, traditional Machine Learning (ML) models often struggle to adapt to dynamic environments. Evolving Machine Learning (EML) has emerged as…