{"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/fast-estimation-of-partial-dependence","title":"Fast Estimation of Partial Dependence Functions using Trees","arxiv_id":"2410.13448","date":"2024-10-17","proceeding":null,"authors":["Jinyang Liu","Tessa Steensgaard","Marvin N. Wright","Niklas Pfister","Munir Hiabu"],"abstract":"Many existing interpretation methods are based on Partial Dependence (PD) functions that, for a pre-trained machine learning model, capture how a subset of the features affects the predictions by averaging over the remaining features. Notable methods include Shapley additive explanations (SHAP) which computes feature contributions based on a game theoretical interpretation and PD plots (i.e., 1-dim PD functions) that capture average marginal main effects. Recent work has connected these approaches using a functional decomposition and argues that SHAP values can be misleading since they merge main and interaction effects into a single local effect. A major advantage of SHAP compared to other PD-based interpretations, however, has been the availability of fast estimation techniques, such as \\texttt{TreeSHAP}. In this paper, we propose a new tree-based estimator, \\texttt{FastPD}, which efficiently estimates arbitrary PD functions. We show that \\texttt{FastPD} consistently estimates the desired population quantity -- in contrast to path-dependent \\texttt{TreeSHAP} which is inconsistent when features are correlated. For moderately deep trees, \\texttt{FastPD} improves the complexity of existing methods from quadratic to linear in the number of observations. By estimating PD functions for arbitrary feature subsets, \\texttt{FastPD} can be used to extract PD-based interpretations such as SHAP, PD plots and higher order interaction effects.","url_abs":"https://arxiv.org/abs/2410.13448v1","url_pdf":"https://arxiv.org/pdf/2410.13448v1.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":"fast-estimation-of-partial-dependence","repo_url":"https://github.com/plantedml/glex","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"fast-estimation-of-partial-dependence","repo_url":"https://github.com/jyliuu/fastpd-reproducibility-code","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"methods":[{"method_slug":"shap","method_name":"SHAP"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2410.13448","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}