{"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/vizml-a-machine-learning-approach-to","title":"VizML: A Machine Learning Approach to Visualization Recommendation","arxiv_id":"1808.04819","date":"2018-08-14","proceeding":null,"authors":["Kevin Z. Hu","Michiel A. Bakker","Stephen Li","Tim Kraska","César A. Hidalgo"],"abstract":"Data visualization should be accessible for all analysts with data, not just\nthe few with technical expertise. Visualization recommender systems aim to\nlower the barrier to exploring basic visualizations by automatically generating\nresults for analysts to search and select, rather than manually specify. Here,\nwe demonstrate a novel machine learning-based approach to visualization\nrecommendation that learns visualization design choices from a large corpus of\ndatasets and associated visualizations. First, we identify five key design\nchoices made by analysts while creating visualizations, such as selecting a\nvisualization type and choosing to encode a column along the X- or Y-axis. We\ntrain models to predict these design choices using one million\ndataset-visualization pairs collected from a popular online visualization\nplatform. Neural networks predict these design choices with high accuracy\ncompared to baseline models. We report and interpret feature importances from\none of these baseline models. To evaluate the generalizability and uncertainty\nof our approach, we benchmark with a crowdsourced test set, and show that the\nperformance of our model is comparable to human performance when predicting\nconsensus visualization type, and exceeds that of other ML-based systems.","url_abs":"http://arxiv.org/abs/1808.04819v1","url_pdf":"http://arxiv.org/pdf/1808.04819v1.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":"vizml-a-machine-learning-approach-to","repo_url":"https://github.com/mitmedialab/vizml","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"data-visualization","task_name":"Data Visualization"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1808.04819","atlas_url":"https://app.syntology.ai/?focus=1808.04819","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}