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autoBagging

Automatic Hyperparameter Optimization of Bagging Workflows

Authored by: Fábio Pinto and Vítor Cerqueira

autoBagging is an R package for automatically optimizing bagging workflows to solve classification predictive tasks.

Installing

Currently, autoBagging is only available in Github. Soon it will be submitted to CRAN.

Install the package using devtools:

  • devtools::install_github("hadley/devtools")

followed by:

  • devtools::install_github("fhpinto/autoBagging")

In some OS, the installation might need manual installation of recursive dependencies (e.g. data.table).

Guidelines

The core function is autoBagging. Its input is simply the formula for the predictive classification task and the dataset:

  • auto_model <- autoBagging(formula, train.data)

For predicting new instances, the model uses the standard predict method:

  • preds <- predict(auto_model, test.data)

Contact us at: {fhpinto, vmac}@inesctec.pt

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Version

Install

install.packages('autoBagging')

Monthly Downloads

163

Version

0.1.0

License

GPL (>= 2)

Maintainer

Vitor Cerqueira

Last Published

July 2nd, 2017

Functions in autoBagging (0.1.0)

ContAttrs

Retrieve names of continuous attributes (not including the target)
GetMeasure

Retrieve the value of a previously computed measure
KNORA.E

K-Nearest-ORAcle-Eliminate
OLA

Overall Local Accuracy
classmajority.landmarker.mutual.information

classmajority.landmarker.mutual.information
dstump.landmarker_d1

dstump.landmarker_d1
dstump.landmarker_d2.entropy

dstump.landmarker_d2.entropy
dstump.landmarker_d2.interinfo

dstump.landmarker_d2.interinfo
ReadDF

FUNCTION TO TRANSFORM DATA FRAME INTO LIST WITH GSI REQUIREMENTS
SymbAttrs

Retrieve names of symbolic attributes (not including the target)
classmajority.landmarker.entropy

classmajority.landmarker.entropy
classmajority.landmarker.interinfo

classmajority.landmarker.interinfo
dstump.landmarker_d2

dstump.landmarker_d2
dstump.landmarker_d2.correlation

dstump.landmarker_d2.correlation
majority_voting

majority voting
mdsq

Margin Distance Minimization
autoBagging

autoBagging
baggedtrees

bagged trees models
classmajority.landmarker

classmajority.landmarker
classmajority.landmarker.correlation

classmajority.landmarker.correlation
dstump.landmarker_d1.interinfo

dstump.landmarker_d1.interinfo
bagging

bagging method
bb

Boosting-based pruning of models
dstump.landmarker_d3.correlation

dstump.landmarker_d3.correlation
dstump.landmarker_d3.entropy

dstump.landmarker_d3.entropy
nb.landmarker.entropy

nb.landmarker.entropy
nb.landmarker.interinfo

nb.landmarker.interinfo
abmodel-class

abmodel-class
abmodel

abmodel
dstump.landmarker_d1.correlation

dstump.landmarker_d1.correlation
dstump.landmarker_d1.entropy

dstump.landmarker_d1.entropy
get_target

get target variable
lda.landmarker.correlation

lda.landmarker.correlation
nb.landmarker.mutual.information

nb.landmarker.mutual.information
predict,abmodel-method

Predicting on new data with a abmodel model
dstump.landmarker_d1.mutual.information

dstump.landmarker_d1.mutual.information
nb.landmarker

nb.landmarker
nb.landmarker.correlation

nb.landmarker.correlation
sysdata

sysdata
dstump.landmarker_d2.mutual.information

dstump.landmarker_d2.mutual.information
dstump.landmarker_d3

dstump.landmarker_d3
dstump.landmarker_d3.interinfo

dstump.landmarker_d3.interinfo
dstump.landmarker_d3.mutual.information

dstump.landmarker_d3.mutual.information