Package: ale 0.3.0
ale: Interpretable Machine Learning and Statistical Inference with Accumulated Local Effects (ALE)
Accumulated Local Effects (ALE) were initially developed as a model-agnostic approach for global explanations of the results of black-box machine learning algorithms. ALE has a key advantage over other approaches like partial dependency plots (PDP) and SHapley Additive exPlanations (SHAP): its values represent a clean functional decomposition of the model. As such, ALE values are not affected by the presence or absence of interactions among variables in a mode. Moreover, its computation is relatively rapid. This package rewrites the original code from the 'ALEPlot' package for calculating ALE data and it completely reimplements the plotting of ALE values. It also extends the original ALE concept to add bootstrap-based confidence intervals and ALE-based statistics that can be used for statistical inference. For more details, see Okoli, Chitu. 2023. “Statistical Inference Using Machine Learning and Classical Techniques Based on Accumulated Local Effects (ALE).” arXiv. <arxiv:2310.09877>. <doi:10.48550/arXiv.2310.09877>.
Authors:
ale_0.3.0.tar.gz
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ale_0.3.0.tgz(r-4.4-any)ale_0.3.0.tgz(r-4.3-any)
ale_0.3.0.tar.gz(r-4.5-noble)ale_0.3.0.tar.gz(r-4.4-noble)
ale_0.3.0.tgz(r-4.4-emscripten)ale_0.3.0.tgz(r-4.3-emscripten)
ale.pdf |ale.html✨
ale/json (API)
NEWS
# Install 'ale' in R: |
install.packages('ale', repos = c('https://tripartio.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/tripartio/ale/issues
Last updated 7 months agofrom:b613c10122. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Sep 16 2024 |
R-4.5-win | OK | Sep 16 2024 |
R-4.5-linux | OK | Sep 16 2024 |
R-4.4-win | OK | Sep 16 2024 |
R-4.4-mac | OK | Sep 16 2024 |
R-4.3-win | OK | Sep 16 2024 |
R-4.3-mac | OK | Sep 16 2024 |
Exports:aleale_ixncreate_p_funsmodel_bootstrap
Dependencies:abindactuarassertthatbackportsbootbroomcarcarDataclicodetoolscolorspacecorrplotcowplotcpp11cvarDerivdigestdoBydplyrellipsisexpintextraDistrfansifarverfastICAfBasicsfGarchfurrrfuturegbutilsgenericsggplot2ggpubrggrepelggsciggsignifglobalsgluegridExtragssgtableinsightisobandlabelinglatticelifecyclelistenvlme4logitnormmagrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmunsellnakagaminlmenloptrnnetnumDerivparallellypbkrtestpillarpkgconfigpolynomprogressrpurrrquantregR6rbibutilsRColorBrewerRcppRcppEigenRdpackrlangrstatixscalesSparseMspatialstablediststringistringrsurvivaltibbletidyrtidyselecttimeDatetimeSeriesunivariateMLutf8vctrsviridisLitewithryaImpute
ale function handling of various datatypes for x
Rendered fromale-x-datatypes.Rmd
usingknitr::rmarkdown
on Sep 16 2024.Last update: 2024-02-13
Started: 2023-09-30
ALE-based statistics for statistical inference and effect sizes
Rendered fromale-statistics.Rmd
usingknitr::rmarkdown
on Sep 16 2024.Last update: 2024-02-13
Started: 2023-09-30
Analyzing small datasets (fewer than 2000 rows) with ALE
Rendered fromale-small-datasets.Rmd
usingknitr::rmarkdown
on Sep 16 2024.Last update: 2024-02-09
Started: 2023-09-30
Introduction to the ale package
Rendered fromale-intro.Rmd
usingknitr::rmarkdown
on Sep 16 2024.Last update: 2024-02-13
Started: 2023-09-30
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Create and return ALE data, statistics, and plots | ale |
Create and return ALE interaction data, statistics, and plots | ale_ixn |
Census Income | census |
Create a p-value functions object that can be used to generate p-values | create_p_funs |
model_bootstrap.R | model_bootstrap |
Multi-variable transformation of the mtcars dataset. | var_cars |