{"url":"/method/macest","slug":"macest","name":"MACEst","full_name":"MACEst","full_name_withheld":false,"description_markdown":"**Model Agnostic Confidence Estimator**, or **MACEst**, is a model-agnostic confidence estimator. Using a set of nearest neighbours, the algorithm differs from other methods by estimating confidence independently as a local quantity which explicitly accounts for both aleatoric and epistemic uncertainty. This approach differs from standard calibration methods that use a global point prediction model as a starting point for the confidence estimate.","description_state":"present","introduced_year":null,"introduced_by":{"title":"MACEst: The reliable and trustworthy Model Agnostic Confidence Estimator","paper":"/paper/macest-the-reliable-and-trustworthy-model","first_author":"Rhys Green","n_authors":3,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/macest-the-reliable-and-trustworthy-model"},"source":{"url":"https://arxiv.org/abs/2109.01531v1","title":"MACEst: The reliable and trustworthy Model Agnostic Confidence Estimator","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"General","area_id":"general","collection":"Confidence Estimators","url":"/methods/category/confidence-estimators","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":1,"papers_newest_first":[{"paper":"/paper/macest-the-reliable-and-trustworthy-model","title":"MACEst: The reliable and trustworthy Model Agnostic Confidence Estimator","date":"2021-09-02","arxiv_id":"2109.01531","n_code_links":1,"syntology":null}],"papers_shown":1,"tasks":[{"task":"/task/machine-learning","name":"BIG-bench Machine Learning","papers":1}],"tasks_shown":1,"n_tasks":1,"usage_by_year":[{"year":"2021","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/macest"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}