{ "_id": "6a113decacfb0bcc41ce602d", "Package": "autoBagging", "Type": "Package", "Title": "Learning to Rank Bagging Workflows with Metalearning", "Version": "0.1.0", "Authors@R": "c(person(\"Fabio\", \"Pinto\", role = c(\"aut\")),\nperson(\"Vitor\", \"Cerqueira\", email = \"cerqueira.vitormanuel@gmail.com\", role = \"cre\"),\nperson(\"Carlos\", \"Soares\", role = \"ctb\"),\nperson(\"Joao\", \"Mendes-Moreira\", role = \"ctb\"))", "Author": "Fabio Pinto [aut], Vitor Cerqueira [cre], Carlos Soares [ctb],\nJoao Mendes-Moreira [ctb]", "Maintainer": "Vitor Cerqueira ", "Description": "A framework for automated machine learning. Concretely,\nthe focus is on the optimisation of bagging workflows. A\nbagging workflows is composed by three phases: (i) generation:\nwhich and how many predictive models to learn; (ii) pruning:\nafter learning a set of models, the worst ones are cut off from\nthe ensemble; and (iii) integration: how the models are\ncombined for predicting a new observation. autoBagging\noptimises these processes by combining metalearning and a\nlearning to rank approach to learn from metadata. It\nautomatically ranks 63 bagging workflows by exploiting past\nperformance and dataset characterization. A complete\ndescription of the method can be found in: Pinto, F.,\nCerqueira, V., Soares, C., Mendes-Moreira, J. (2017):\n\"autoBagging: Learning to Rank Bagging Workflows with\nMetalearning\" arXiv preprint arXiv:1706.09367.", "License": "GPL (>= 2)", "Encoding": "UTF-8", "LazyData": "no", "RoxygenNote": "6.0.1", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-23 05:33:24 UTC", "User": "root" }, "Config/pak/sysreqs": "make libicu-dev", "Repository": "https://vcerqueira.r-universe.dev", "Date/Publication": "2017-07-01 23:06:44 UTC", "RemoteUrl": "https://github.com/cran/autoBagging", "RemoteRef": "HEAD", "RemoteSha": "557d304a4a7bd0bf707d9c1a1f6d6a35e8b9da98", "MD5sum": "e755019665c462c49612a86e4ba619ee", "_user": "vcerqueira", "_type": "src", "_file": "autoBagging_0.1.0.tar.gz", "_fileid": "c784061049f01e0dcc90d7d42b5cb39e32d6eae448c32e27eb046db3a52d4f9d", "_filesize": 922227, "_sha256": "c784061049f01e0dcc90d7d42b5cb39e32d6eae448c32e27eb046db3a52d4f9d", "_created": "2026-05-23T05:33:24.000Z", "_published": "2026-05-23T05:41:00.503Z", "_distro": "noble", "_jobs": [ { "job": 77500122378, 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