ingredients: Effects and Importances of Model Ingredients

Collection of tools for assessment of feature importance and feature effects. Key functions are: feature_importance() for assessment of global level feature importance, ceteris_paribus() for calculation of the what-if plots, partial_dependence() for partial dependence plots, conditional_dependence() for conditional dependence plots, accumulated_dependence() for accumulated local effects plots, aggregate_profiles() and cluster_profiles() for aggregation of ceteris paribus profiles, generic print() and plot() for better usability of selected explainers, generic plotD3() for interactive, D3 based explanations, and generic describe() for explanations in natural language. The package 'ingredients' is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.

Version: 2.0
Depends: R (≥ 3.5)
Imports: ggplot2, scales, gridExtra, methods
Suggests: DALEX, gower, randomForest, testthat, r2d3, jsonlite, knitr, rmarkdown, covr
Published: 2020-09-01
Author: Przemyslaw Biecek ORCID iD [aut, cre], Hubert Baniecki ORCID iD [aut], Adam Izdebski [aut]
Maintainer: Przemyslaw Biecek <przemyslaw.biecek at>
License: GPL-3
NeedsCompilation: no
Materials: NEWS
CRAN checks: ingredients results


Reference manual: ingredients.pdf
Vignettes: Explanations in natural language
Simulated data, real problem
General introduction: Survival on the RMS Titanic
Package source: ingredients_2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: ingredients_2.0.tgz, r-oldrel: ingredients_2.0.tgz
Old sources: ingredients archive

Reverse dependencies:

Reverse imports: arenar, corrgrapher, DALEX, drifter, localModel, modelStudio
Reverse suggests: DALEXtra, vivo


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