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autoBagging  

Learning to Rank Bagging Workflows with Metalearning
View on CRAN: Click here


Download and install autoBagging package within the R console
Install from CRAN:
install.packages("autoBagging")

Install from Github:
library("remotes")
install_github("cran/autoBagging")

Install by package version:
library("remotes")
install_version("autoBagging", "0.1.0")



Attach the package and use:
library("autoBagging")
Maintained by
Vitor Cerqueira
[Scholar Profile | Author Map]
First Published: 2017-07-02
Latest Update: 2017-07-02
Description:
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.
How to cite:
Vitor Cerqueira (2017). autoBagging: Learning to Rank Bagging Workflows with Metalearning. R package version 0.1.0, https://cran.r-project.org/web/packages/autoBagging. Accessed 06 Apr. 2025.
Previous versions and publish date:
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Other packages that cited autoBagging R package
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Complete documentation for autoBagging
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