Dictionary of Applied Machine Learning
A baseline is a reference level against which the performance of a trained model is compared. This comparison allows verifying whether the achieved average loss is satisfactory, i.e., whether the learned hypothesis is already close to optimal. Baselines can be obtained from human performance, from an existing machine learning (ML) method, or from an accurate probabilistic model for the data generation. Given such a probabilistic model, the smallest achievable risk is the Bayes risk, incurred by the Bayes estimator of the label given the features. In practice, however, the Bayes estimator is infeasible for at least two reasons. First, the probability distribution of the data generation process might be impossible to determine. Second, computing the Bayes estimator might be computationally too expensive.
Python demo — a script that recomputes what the entry states
Full entry (PDF) — definition, figures, worked examples, and references.
@misc{dictml_baseline,
author = {Jung, Alexander and Olioumtsevits, Konstantina and Schnoor, Ekkehard},
title = {baseline},
howpublished = {Dictionary of Applied Machine Learning (course edition)},
year = {2026},
doi = {10.5281/zenodo.21569296},
note = {ISBN 978-952-64-3013-3, CC BY 4.0, retrieved 2026-09-11},
url = {https://dictionaryofml.org/terms/baseline.html}
}