Random Baseline

Random baselines serve as crucial benchmarks in evaluating machine learning models, providing a reference point against which algorithm performance can be objectively assessed. Recent research focuses on developing more robust random baselines, particularly addressing limitations in scenarios with small datasets or repeated validation set usage, as well as exploring their application across diverse tasks such as in-context learning, selective backpropagation, and anomaly detection. These improvements enhance the reliability of model evaluations, leading to more accurate assessments of algorithmic progress and facilitating the development of more effective machine learning systems.