{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/probability-series-expansion-classifier-that","title":"Probability Series Expansion Classifier that is Interpretable by Design","arxiv_id":"1710.10301","date":"2017-10-27","proceeding":null,"authors":["Sapan Agarwal","Corey M. Hudson"],"abstract":"This work presents a new classifier that is specifically designed to be fully\ninterpretable. This technique determines the probability of a class outcome,\nbased directly on probability assignments measured from the training data. The\naccuracy of the predicted probability can be improved by measuring more\nprobability estimates from the training data to create a series expansion that\nrefines the predicted probability. We use this work to classify four standard\ndatasets and achieve accuracies comparable to that of Random Forests. Because\nthis technique is interpretable by design, it is capable of determining the\ncombinations of features that contribute to a particular classification\nprobability for individual cases as well as the weightings of each of\ncombination of features.","url_abs":"http://arxiv.org/abs/1710.10301v1","url_pdf":"http://arxiv.org/pdf/1710.10301v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"probability-series-expansion-classifier-that","repo_url":"https://github.com/sandialabs/aweml","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}