Package: triplot 1.3.1
Katarzyna Pekala
triplot: Explaining Correlated Features in Machine Learning Models
Tools for exploring effects of correlated features in predictive models. The predict_triplot() function delivers instance-level explanations that calculate the importance of the groups of explanatory variables. The model_triplot() function delivers data-level explanations. The generic plot function visualises in a concise way importance of hierarchical groups of predictors. All of the the tools are model agnostic, therefore works for any predictive machine learning models. Find more details in Biecek (2018) <arxiv:1806.08915>.
Authors:
triplot_1.3.1.tar.gz
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triplot.pdf |triplot.html✨
triplot/json (API)
NEWS
# Install 'triplot' in R: |
install.packages('triplot', repos = c('https://modeloriented.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/modeloriented/triplot/issues
explanationsexplanatory-model-analysismachine-learningmodel-visualizationxai
Last updated 4 years agofrom:a9721315b1. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 21 2024 |
R-4.5-win | OK | Nov 21 2024 |
R-4.5-linux | OK | Nov 21 2024 |
R-4.4-win | OK | Nov 21 2024 |
R-4.4-mac | OK | Nov 21 2024 |
R-4.3-win | OK | Nov 21 2024 |
R-4.3-mac | OK | Nov 21 2024 |
Exports:aspect_importanceaspect_importance_singlecalculate_triplotcluster_variablesget_samplegroup_variableshierarchical_importancelimelist_variablesmodel_triplotpredict_aspectspredict_triplot
Dependencies:clicodetoolscolorspaceDALEXfansifarverforeachggdendroggplot2glmnetgluegridExtragtableiBreakDowningredientsisobanditeratorskernelshaplabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepatchworkpillarpkgconfigR6RColorBrewerRcppRcppEigenrlangscalesshapesurvivaltibbleutf8vctrsviridisLitewithr