Package: autoBagging 0.1.0
autoBagging: Learning to Rank Bagging Workflows with Metalearning
A framework for automated machine learning. Concretely, the focus is on the optimisation of bagging workflows. A bagging workflows is composed by three phases: (i) generation: which and how many predictive models to learn; (ii) pruning: after learning a set of models, the worst ones are cut off from the ensemble; and (iii) integration: how the models are combined for predicting a new observation. autoBagging optimises these processes by combining metalearning and a learning to rank approach to learn from metadata. It automatically ranks 63 bagging workflows by exploiting past performance and dataset characterization. A complete description of the method can be found in: Pinto, F., Cerqueira, V., Soares, C., Mendes-Moreira, J. (2017): "autoBagging: Learning to Rank Bagging Workflows with Metalearning" arXiv preprint arXiv:1706.09367.
Authors:
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autoBagging.pdf |autoBagging.html✨
autoBagging/json (API)
# Install 'autoBagging' in R: |
install.packages('autoBagging', repos = c('https://vcerqueira.r-universe.dev', 'https://cloud.r-project.org')) |
- sysdata - Sysdata
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 7 years agofrom:557d304a4a. Checks:OK: 1 NOTE: 6. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 11 2024 |
R-4.5-win | NOTE | Oct 11 2024 |
R-4.5-linux | NOTE | Oct 11 2024 |
R-4.4-win | NOTE | Oct 11 2024 |
R-4.4-mac | NOTE | Oct 11 2024 |
R-4.3-win | NOTE | Oct 11 2024 |
R-4.3-mac | NOTE | Oct 11 2024 |
Exports:abmodelautoBaggingbaggedtreesbaggingget_targetmajority_votingpredict
Dependencies:abindcaretclasscliclockclustercodetoolscoincolorspaceCORElearncpp11data.tablediagramdigestdplyre1071entropyfansifarverforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatinfotheoipredisobanditeratorsjsonliteKernSmoothlabelinglatticelavalibcoinlifecyclelistenvlsrlubridatemagrittrMASSMatrixmatrixStatsmgcvminervaModelMetricsmodeltoolsmultcompmunsellmvtnormnlmennetnumDerivparallellypartypillarpkgconfigplotrixplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcppRcppArmadillorecipesreshape2rlangrpartrpart.plotsandwichscalesshapeSQUAREMstringistringrstrucchangesurvivalTH.datatibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithrxgboostzoo