Package: fmeffects 0.1.4

Holger Löwe

fmeffects: Model-Agnostic Interpretations with Forward Marginal Effects

Create local, regional, and global explanations for any machine learning model with forward marginal effects. You provide a model and data, and 'fmeffects' computes feature effects. The package is based on the theory in: C. A. Scholbeck, G. Casalicchio, C. Molnar, B. Bischl, and C. Heumann (2022) <doi:10.48550/arXiv.2201.08837>.

Authors:Holger Löwe [cre, aut], Christian Scholbeck [aut], Christian Heumann [rev], Bernd Bischl [rev], Giuseppe Casalicchio [rev]

fmeffects_0.1.4.tar.gz
fmeffects_0.1.4.tar.gz(r-4.5-noble)fmeffects_0.1.4.tar.gz(r-4.4-noble)
fmeffects_0.1.4.tgz(r-4.4-emscripten)fmeffects_0.1.4.tgz(r-4.3-emscripten)
fmeffects.pdf |fmeffects.html
fmeffects/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/holgstr/fmeffects/issues

Datasets:
  • bikes - Regression data of the usage of rental bikes in Washington D.C., USA

3.53 score 6 scripts 1.1k downloads 14 exports 57 dependencies

Last updated 16 days agofrom:d5bdaf73dc. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 06 2024
R-4.5-linuxOKNov 06 2024

Exports:ameAverageMarginalEffectscamefmeForwardMarginalEffectmakePredictorPartitioningPartitioningCtreePartitioningRpartPredictorPredictorCaretPredictorLMPredictorMLR3PredictorParsnip

Dependencies:backportsbriocallrcheckmateclicolorspacecowplotcrayondata.tabledescdiffobjdigestevaluatefansifarverFormulafsggpartyggplot2gluegtableinumisobandjsonlitelabelinglatticelibcoinlifecyclemagrittrMASSMatrixmgcvmunsellmvtnormnlmepartykitpillarpkgbuildpkgconfigpkgloadpraiseprocessxpsR6RColorBrewerrlangrpartrprojrootscalessurvivaltestthattibbleutf8vctrsviridisLitewaldowithr

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Last update: 2023-09-26
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