{"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/sparse-partially-linear-additive-models","title":"Sparse Partially Linear Additive Models","arxiv_id":"1407.4729","date":"2014-07-17","proceeding":null,"authors":["Yin Lou","Jacob Bien","Rich Caruana","Johannes Gehrke"],"abstract":"The generalized partially linear additive model (GPLAM) is a flexible and\ninterpretable approach to building predictive models. It combines features in\nan additive manner, allowing each to have either a linear or nonlinear effect\non the response. However, the choice of which features to treat as linear or\nnonlinear is typically assumed known. Thus, to make a GPLAM a viable approach\nin situations in which little is known $a~priori$ about the features, one must\novercome two primary model selection challenges: deciding which features to\ninclude in the model and determining which of these features to treat\nnonlinearly. We introduce the sparse partially linear additive model (SPLAM),\nwhich combines model fitting and $both$ of these model selection challenges\ninto a single convex optimization problem. SPLAM provides a bridge between the\nlasso and sparse additive models. Through a statistical oracle inequality and\nthorough simulation, we demonstrate that SPLAM can outperform other methods\nacross a broad spectrum of statistical regimes, including the high-dimensional\n($p\\gg N$) setting. We develop efficient algorithms that are applied to real\ndata sets with half a million samples and over 45,000 features with excellent\npredictive performance.","url_abs":"http://arxiv.org/abs/1407.4729v3","url_pdf":"http://arxiv.org/pdf/1407.4729v3.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":"sparse-partially-linear-additive-models","repo_url":"https://github.com/yinlou/mltk","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"additive-models","task_name":"Additive models"},{"task_slug":"model-selection","task_name":"Model Selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1407.4729","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}