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:Fabio Pinto [aut], Vitor Cerqueira [cre], Carlos Soares [ctb], Joao Mendes-Moreira [ctb]

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autoBagging/json (API)

# Install 'autoBagging' in R:
install.packages('autoBagging', repos = c('https://vcerqueira.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.70 score 9 scripts 241 downloads 7 exports 98 dependencies

Last updated 7 years agofrom:557d304a4a. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 10 2024
R-4.5-winNOTENov 10 2024
R-4.5-linuxNOTENov 10 2024
R-4.4-winNOTENov 10 2024
R-4.4-macNOTENov 10 2024
R-4.3-winNOTENov 10 2024
R-4.3-macNOTENov 10 2024

Exports:abmodelautoBaggingbaggedtreesbaggingget_targetmajority_votingpredict

Dependencies:abindcaretclasscliclockclustercodetoolscoincolorspaceCORElearncpp11data.tablediagramdigestdplyre1071entropyfansifarverforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatinfotheoipredisobanditeratorsjsonliteKernSmoothlabelinglatticelavalibcoinlifecyclelistenvlsrlubridatemagrittrMASSMatrixmatrixStatsmgcvminervaModelMetricsmodeltoolsmultcompmunsellmvtnormnlmennetnumDerivparallellypartypillarpkgconfigplotrixplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcppRcppArmadillorecipesreshape2rlangrpartrpart.plotsandwichscalesshapeSQUAREMstringistringrstrucchangesurvivalTH.datatibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithrxgboostzoo

Readme and manuals

Help Manual

Help pageTopics
abmodelabmodel
abmodel-classabmodel-class
autoBaggingautoBagging-package autoBagging
bagged trees modelsbaggedtrees
bagging methodbagging
Boosting-based pruning of modelsbb
classmajority.landmarkerclassmajority.landmarker
classmajority.landmarker.correlationclassmajority.landmarker.correlation
classmajority.landmarker.entropyclassmajority.landmarker.entropy
classmajority.landmarker.interinfoclassmajority.landmarker.interinfo
classmajority.landmarker.mutual.informationclassmajority.landmarker.mutual.information
Retrieve names of continuous attributes (not including the target)ContAttrs
dstump.landmarker_d1dstump.landmarker_d1
dstump.landmarker_d1.correlationdstump.landmarker_d1.correlation
dstump.landmarker_d1.entropydstump.landmarker_d1.entropy
dstump.landmarker_d1.interinfodstump.landmarker_d1.interinfo
dstump.landmarker_d1.mutual.informationdstump.landmarker_d1.mutual.information
dstump.landmarker_d2dstump.landmarker_d2
dstump.landmarker_d2.correlationdstump.landmarker_d2.correlation
dstump.landmarker_d2.entropydstump.landmarker_d2.entropy
dstump.landmarker_d2.interinfodstump.landmarker_d2.interinfo
dstump.landmarker_d2.mutual.informationdstump.landmarker_d2.mutual.information
dstump.landmarker_d3dstump.landmarker_d3
dstump.landmarker_d3.correlationdstump.landmarker_d3.correlation
dstump.landmarker_d3.entropydstump.landmarker_d3.entropy
dstump.landmarker_d3.interinfodstump.landmarker_d3.interinfo
dstump.landmarker_d3.mutual.informationdstump.landmarker_d3.mutual.information
get target variableget_target
Retrieve the value of a previously computed measureGetMeasure
K-Nearest-ORAcle-EliminateKNORA.E
lda.landmarker.correlationlda.landmarker.correlation
majority votingmajority_voting
Margin Distance Minimizationmdsq
nb.landmarkernb.landmarker
nb.landmarker.correlationnb.landmarker.correlation
nb.landmarker.entropynb.landmarker.entropy
nb.landmarker.interinfonb.landmarker.interinfo
nb.landmarker.mutual.informationnb.landmarker.mutual.information
Overall Local AccuracyOLA
Predicting on new data with a *abmodel* modelpredict,abmodel-method
FUNCTION TO TRANSFORM DATA FRAME INTO LIST WITH GSI REQUIREMENTSReadDF
Retrieve names of symbolic attributes (not including the target)SymbAttrs
sysdatasysdata