{"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/probabilistic-random-forest-a-machine","title":"Probabilistic Random Forest: A machine learning algorithm for noisy datasets","arxiv_id":"1811.05994","date":"2018-11-14","proceeding":null,"authors":["Itamar Reis","Dalya Baron","Sahar Shahaf"],"abstract":"Machine learning (ML) algorithms become increasingly important in the\nanalysis of astronomical data. However, since most ML algorithms are not\ndesigned to take data uncertainties into account, ML based studies are mostly\nrestricted to data with high signal-to-noise ratio. Astronomical datasets of\nsuch high-quality are uncommon. In this work we modify the long-established\nRandom Forest (RF) algorithm to take into account uncertainties in the\nmeasurements (i.e., features) as well as in the assigned classes (i.e.,\nlabels). To do so, the Probabilistic Random Forest (PRF) algorithm treats the\nfeatures and labels as probability distribution functions, rather than\ndeterministic quantities. We perform a variety of experiments where we inject\ndifferent types of noise to a dataset, and compare the accuracy of the PRF to\nthat of RF. The PRF outperforms RF in all cases, with a moderate increase in\nrunning time. We find an improvement in classification accuracy of up to 10% in\nthe case of noisy features, and up to 30% in the case of noisy labels. The PRF\naccuracy decreased by less then 5% for a dataset with as many as 45%\nmisclassified objects, compared to a clean dataset. Apart from improving the\nprediction accuracy in noisy datasets, the PRF naturally copes with missing\nvalues in the data, and outperforms RF when applied to a dataset with different\nnoise characteristics in the training and test sets, suggesting that it can be\nused for Transfer Learning.","url_abs":"http://arxiv.org/abs/1811.05994v1","url_pdf":"http://arxiv.org/pdf/1811.05994v1.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":"probabilistic-random-forest-a-machine","repo_url":"https://github.com/ireis/PRF","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"missing-values","task_name":"Missing Values"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"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}