Package: modelStudio 3.1.2.9000

Hubert Baniecki

modelStudio: Interactive Studio for Explanatory Model Analysis

Automate the explanatory analysis of machine learning predictive models. Generate advanced interactive model explanations in the form of a serverless HTML site with only one line of code. This tool is model-agnostic, therefore compatible with most of the black-box predictive models and frameworks. The main function computes various (instance and model-level) explanations and produces a customisable dashboard, which consists of multiple panels for plots with their short descriptions. It is possible to easily save the dashboard and share it with others. 'modelStudio' facilitates the process of Interactive Explanatory Model Analysis introduced in Baniecki et al. (2023) <doi:10.1007/s10618-023-00924-w>.

Authors:Hubert Baniecki [aut, cre], Przemyslaw Biecek [aut], Piotr Piatyszek [ctb]

modelStudio_3.1.2.9000.tar.gz
modelStudio_3.1.2.9000.zip(r-4.7)modelStudio_3.1.2.9000.zip(r-4.6)modelStudio_3.1.2.9000.zip(r-4.5)
modelStudio_3.1.2.9000.tgz(r-4.6-any)modelStudio_3.1.2.9000.tgz(r-4.5-any)
modelStudio_3.1.2.9000.tar.gz(r-4.7-any)modelStudio_3.1.2.9000.tar.gz(r-4.6-any)
modelStudio_3.1.2.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
modelStudio/json (API)

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

Bug tracker:https://github.com/modeloriented/modelstudio/issues

Pkgdown/docs site:https://modelstudio.drwhy.ai

Datasets:

On CRAN:

Conda:

aiexplainableexplainable-aiexplainable-machine-learningexplanatory-model-analysishumanimlinteractiveinteractivityinterpretabilityinterpretableinterpretable-machine-learninglearningmachinemodelmodel-visualizationvisualizationxai

8.03 score 333 stars 72 scripts 442 downloads 1 mentions 5 exports 60 dependencies

Last updated from:62d23a802f. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK261
source / vignettesOK264
linux-release-x86_64OK226
macos-release-arm64OK213
macos-oldrel-arm64OK137
windows-develOK180
windows-releaseOK221
windows-oldrelOK228
wasm-releaseOK123

Exports:modelStudioms_merge_observationsms_optionsms_update_observationsms_update_options

Dependencies:base64encbslibcachemclicodetoolscpp11crayonDALEXdigestdoFutureevaluatefarverfastmapfontawesomeforeachfsfuturefuture.applyggplot2globalsgluegridExtragtablehighrhmshtmltoolshtmlwidgetsiBreakDowningredientsisobanditeratorsjquerylibjsonlitekernelshapknitrlabelinglifecyclelistenvmemoisemimeparallellypkgconfigprettyunitsprogressr2d3R6rappdirsRColorBrewerrlangrmarkdownrstudioapiS7sassscalestinytexvctrsviridisLitewithrxfunyaml

modelStudio in R Markdown HTML
Rendered fromms-rmarkdown.Rmdusingknitr::rmarkdown

Last update: 2021-07-09
Started: 2021-07-09

modelStudio - R & Python examples
R & Python Examples | R | mlr dashboard | mlr3 dashboard | xgboost dashboard | caret dashboard | h2o dashboard | parsnip dashboard | tidymodels dashboard | Python | scikit-learn dashboard | lightgbm dashboard | keras/tensorflow dashboard | References
Rendered fromms-r-python-examples.Rmdusingknitr::rmarkdown

Last update: 2021-07-08
Started: 2020-04-11

modelStudio - perks and features
modelStudio parameters | instance explanations | grid size | animations | more calculations means more time | no EDA mode | progress bar | viewer or browser? | parallel computation | additional options | update observations | Shiny | DALEXtra | References
Rendered fromms-perks-features.Rmdusingknitr::rmarkdown

Last update: 2021-07-08
Started: 2020-04-11