{"id":17272470,"url":"https://github.com/hbldh/skboost","last_synced_at":"2025-04-14T08:20:55.021Z","repository":{"id":145584241,"uuid":"51300840","full_name":"hbldh/skboost","owner":"hbldh","description":"MILBoost and other boosting algorithms, compatible with scikit-learn","archived":false,"fork":false,"pushed_at":"2024-11-15T15:47:18.000Z","size":1435,"stargazers_count":14,"open_issues_count":0,"forks_count":12,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-03-27T21:52:06.915Z","etag":null,"topics":["boosting","boosting-algorithms","machine-learning","scikit-learn"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/hbldh.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2016-02-08T13:57:24.000Z","updated_at":"2024-11-15T15:47:22.000Z","dependencies_parsed_at":null,"dependency_job_id":"3f9118c8-bbff-49c7-a7ad-c6383023bc27","html_url":"https://github.com/hbldh/skboost","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hbldh%2Fskboost","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hbldh%2Fskboost/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hbldh%2Fskboost/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hbldh%2Fskboost/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/hbldh","download_url":"https://codeload.github.com/hbldh/skboost/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248844036,"owners_count":21170505,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["boosting","boosting-algorithms","machine-learning","scikit-learn"],"created_at":"2024-10-15T08:48:43.866Z","updated_at":"2025-04-14T08:20:54.996Z","avatar_url":"https://github.com/hbldh.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# skboost\n\n[![Build and test](https://github.com/hbldh/skboost/actions/workflows/python-package.yml/badge.svg)](https://github.com/hbldh/skboost/actions/workflows/python-package.yml)\n\nBoosting Algorithms compatible with [scikit-learn](http://scikit-learn.org/).\n\n## Boosting Algorithms\n\nThe `skboost` package contains implementations of some boosting algorithms that\nare outside the scope of [scikit-learn](http://scikit-learn.org/).\n\nThe main point of interest is the MILBoost algorithm, which performs boosting\nwith a Multiple Instance Learning formulation.\n\n### MILBoost\n\nSee \\[1\\], \\[2\\] and \\[4\\].\n\n### GentleBoost\n\nSee \\[3\\].\n\n### LogitBoost\n\nSee \\[3\\].\n\n## Datasets\n\nThis repository includes a vendored copy of the MUSK datasets (\\[5\\]), both version 1 and version 2.\nThese are used for multiple instance learning benchmarks:\n\n\u003e This dataset describes a set of 92 molecules of which 47 are judged by human experts \n\u003e to be musks and the remaining 45 molecules are judged to be non-musks. The goal is \n\u003e to learn to predict whether new molecules will be musks or non-musks. However, the 166 \n\u003e features that describe these molecules depend upon the exact shape, or conformation, \n\u003e of the molecule. Because bonds can rotate, a single molecule can adopt many different \n\u003e shapes. To generate this data set, the low-energy conformations of the molecules were \n\u003e generated and then filtered to remove highly similar conformations. This left 476 \n\u003e conformations. Then, a feature vector was extracted that describes each conformation. \n\u003e\n\u003e This many-to-one relationship between feature vectors and molecules is \n\u003e called the \"multiple instance problem\". When learning a classifier for this data, \n\u003e the classifier should classify a molecule as \"musk\" if ANY of its conformations is \n\u003e classified as a musk. A molecule should be classified as \"non-musk\" if NONE of its \n\u003e conformations is classified as a musk.\n\n## References\n\n\\[1\\] [B. Babenko, P. Dollar, Z. Tu, and S. Belongie. Simultaneous learning\nand alignment: Multi-instance and multi-pose learning. In Faces in\nReal-Life Images, October 2008.](http://vision.ucsd.edu/~pdollar/research/papers/BabenkoEtAlECCV08simul.pdf)\n\n\\[2\\] [Babenko, B.; Ming-Hsuan Yang; Belongie, S., \"Robust Object Tracking \nwith Online Multiple Instance Learning,\" in Pattern Analysis and Machine \nIntelligence, IEEE Transactions on , vol.33, no.8, pp.1619-1632, Aug. 2011\ndoi: 10.1109/TPAMI.2010.226](http://vision.ucsd.edu/~bbabenko/data/miltrack-pami-final.pdf)\n\n\\[3\\] [Friedman, Jerome, Hastie, Trevor, Tibshirani, Robert \u0026 others (2000). \nAdditive logistic regression: a statistical view of \nboosting (with discussion and a rejoinder by the authors). \nThe annals of statistics, 28, 337-407.](https://web.stanford.edu/~hastie/Papers/AdditiveLogisticRegression/alr.pdf)\n\n\\[4\\] [Paul Viola, John C. Platt, and Cha Zhang. Multiple instance boosting\nfor object detection. In In NIPS 18, pages 1419–1426. MIT Press, 2006.](http://vision.ucsd.edu/~bbabenko/data/miltrack-pami-final.pdf)\n\n\\[5\\] Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml](http://archive.ics.uci.edu/ml). \nIrvine, CA: University of California, School of Information and Computer Science.\n\n### Other references\n\n\\[6\\] C.M. Bishop. Pattern recognition and machine learning. Information\nscience and statistics. Springer, 2006.\n\n\\[7\\] Stephen Boyd and Lieven Vandenberghe. Convex Optimization. \nCambridge University Press, March 2004.\n\n\\[8\\] Thomas G. Dietterich and Richard H. Lathrop. Solving the multiple-\ninstance problem with axis-parallel rectangles. Artificial Intelligence,\n89:31–71, 1997.\n\n\\[9\\] Yoav Freund and Robert E. Schapire. A short introduction to boosting,\n1999.\n\n\\[10\\] Jerome H. Friedman. Stochastic gradient boosting. Computational\nStatistics and Data Analysis, 38:367–378, 1999.\n\n\\[11\\] Jerome H. Friedman. Greedy function approximation: A gradient\nboosting machine. Annals of Statistics, 29:1189–1232, 2000.\n\n\\[12\\] James D. Keeler, David E. Rumelhart, and Wee Kheng Leow. Integrated \nsegmentation and recognition of hand-printed numerals. In\nNIPS’90, pages 557–563, 1990.\n\n\\[13\\] Llew Mason, Jonathan Baxter, Peter Bartlett, and Marcus Frean.\nBoosting algorithms as gradient descent in function space, 1999.\n\n\\[14\\] William H. Press, Saul A. Teukolsky, William T. Vetterling, and\nBrian P. Flannery. Numerical Recipes 3rd Edition: The Art of Sci-\nentific Computing. Cambridge University Press, New York, NY, USA,\n3 edition, 2007.\n\n\\[15\\] Vladimir N. Vapnik. The nature of statistical learning theory. Springer-\nVerlag New York, Inc., New York, NY, USA, 1995.\n\n\\[16\\] Paul Viola and Michael Jones. Robust real-time object detection. \nInternational Journal of Computer Vision, 57(2):137–154, 2002.\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhbldh%2Fskboost","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhbldh%2Fskboost","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhbldh%2Fskboost/lists"}