{"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/nls-an-accurate-and-yet-easy-to-interpret","title":"NLS: an accurate and yet easy-to-interpret regression method","arxiv_id":"1910.05206","date":"2019-10-11","proceeding":null,"authors":["Victor Coscrato","Marco Henrique de Almeida Inácio","Tiago Botari","Rafael Izbicki"],"abstract":"An important feature of successful supervised machine learning applications is to be able to explain the predictions given by the regression or classification model being used. However, most state-of-the-art models that have good predictive power lead to predictions that are hard to interpret. Thus, several model-agnostic interpreters have been developed recently as a way of explaining black-box classifiers. In practice, using these methods is a slow process because a novel fitting is required for each new testing instance, and several non-trivial choices must be made. We develop NLS (neural local smoother), a method that is complex enough to give good predictions, and yet gives solutions that are easy to be interpreted without the need of using a separate interpreter. The key idea is to use a neural network that imposes a local linear shape to the output layer. We show that NLS leads to predictive power that is comparable to state-of-the-art machine learning models, and yet is easier to interpret.","url_abs":"https://arxiv.org/abs/1910.05206v1","url_pdf":"https://arxiv.org/pdf/1910.05206v1.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":"nls-an-accurate-and-yet-easy-to-interpret","repo_url":"https://github.com/randommm/nnlocallinear","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"regression-1","task_name":"regression"}],"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}