{"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/user-modelling-for-avoiding-overfitting-in","title":"User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction","arxiv_id":"1710.04881","date":"2017-10-13","proceeding":null,"authors":["Pedram Daee","Tomi Peltola","Aki Vehtari","Samuel Kaski"],"abstract":"In human-in-the-loop machine learning, the user provides information beyond\nthat in the training data. Many algorithms and user interfaces have been\ndesigned to optimize and facilitate this human--machine interaction; however,\nfewer studies have addressed the potential defects the designs can cause.\nEffective interaction often requires exposing the user to the training data or\nits statistics. The design of the system is then critical, as this can lead to\ndouble use of data and overfitting, if the user reinforces noisy patterns in\nthe data. We propose a user modelling methodology, by assuming simple rational\nbehaviour, to correct the problem. We show, in a user study with 48\nparticipants, that the method improves predictive performance in a sparse\nlinear regression sentiment analysis task, where graded user knowledge on\nfeature relevance is elicited. We believe that the key idea of inferring user\nknowledge with probabilistic user models has general applicability in guarding\nagainst overfitting and improving interactive machine learning.","url_abs":"http://arxiv.org/abs/1710.04881v2","url_pdf":"http://arxiv.org/pdf/1710.04881v2.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":"user-modelling-for-avoiding-overfitting-in","repo_url":"https://github.com/HIIT/human-overfitting-in-IML","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1710.04881","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}