basemodels: Baseline Models for Classification and Regression

Providing equivalent functions for the dummy classifier and regressor used in 'Python' 'scikit-learn' library. Our goal is to allow R users to easily identify baseline performance for their classification and regression problems. Our baseline models use no predictors, and are useful in cases of class imbalance, multiclass classification, and when users want to quickly identify how much improvement their statistical and machine learning models are over several baseline models. We use a "better" default (proportional guessing) for the dummy classifier than the 'Python' implementation ("prior", which is the most frequent class in the training set). The functions in the package can be used on their own, or introduce methods named 'dummy_regressor' or 'dummy_classifier' that can be used within the caret package pipeline.

Version: 1.1.0
Imports: stats
Suggests: caret, knitr, rmarkdown
Published: 2023-08-09
Author: Ying-Ju Chen ORCID iD [aut, cre], Fadel M. Megahed ORCID iD [aut], L. Allison Jones-Farmer ORCID iD [aut], Steven E. Rigdon ORCID iD [aut]
Maintainer: Ying-Ju Chen <ychen4 at udayton.edu>
License: MIT + file LICENSE
URL: https://github.com/Ying-Ju/basemodels
NeedsCompilation: no
Citation: basemodels citation info
Materials: README
CRAN checks: basemodels results

Documentation:

Reference manual: basemodels.pdf
Vignettes: Introduction to basemodels

Downloads:

Package source: basemodels_1.1.0.tar.gz
Windows binaries: r-prerel: basemodels_1.1.0.zip, r-release: basemodels_1.1.0.zip, r-oldrel: basemodels_1.1.0.zip
macOS binaries: r-prerel (arm64): basemodels_1.1.0.tgz, r-release (arm64): basemodels_1.1.0.tgz, r-oldrel (arm64): basemodels_1.1.0.tgz, r-prerel (x86_64): basemodels_1.1.0.tgz, r-release (x86_64): basemodels_1.1.0.tgz
Old sources: basemodels archive

Linking:

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