Baseline Algorithm

Baseline algorithms serve as fundamental comparison points in evaluating the performance of new methods across diverse machine learning tasks, from fairness in classification to efficient path planning in robotics and reinforcement learning. Current research emphasizes rigorous testing of baselines, particularly concerning their robustness to factors like data drift and the impact of different evaluation metrics. This focus on robust and well-defined baselines is crucial for ensuring the validity and reproducibility of research findings, ultimately leading to more reliable and impactful advancements in various fields.