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_dependency() for partial dependency plots, conditional_dependency() for conditional dependency plots, accumulated_dependency() 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) .


News

ingredients 0.3.1

  • added references to PM VEE
  • partial_profiles(), accumulated_profiles() and conditional_profiles for variable effects
  • major changes in function names and file names

ingredients 0.3

  • ceteris_paribus_2d extends classical ceteris paribus profiles
  • ceteris_paribus_oscillations calculates oscilations for ceteris paribus profiles
  • fixed examples and file names

ingredients 0.2

  • cluster_profiles helps to identify interactions
  • partial_dependency calculates partial dependency plots
  • aggregate_profiles calculates partial dependency plots and much more

ingredients 0.1

  • port of model_feature_importance and model_feature_response from DALEX to ingredients
  • added tests

Reference manual

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install.packages("ingredients")

0.3.9 by Przemyslaw Biecek, 2 months ago


https://ModelOriented.github.io/ingredients/, https://github.com/ModelOriented/ingredients


Report a bug at https://github.com/ModelOriented/ingredients/issues


Browse source code at https://github.com/cran/ingredients


Authors: Przemyslaw Biecek [aut, cre] , Hubert Baniecki [aut] , Adam Izdebski [aut] , Katarzyna Pekala [aut]


Documentation:   PDF Manual  


GPL license


Imports DALEX, ggplot2, glmnet, scales

Suggests gower, randomForest, xgboost, testthat, r2d3, ggpubr, jsonlite, knitr, rmarkdown


Imported by drifter, localModel, modelStudio, vivo.

Suggested by DALEX.


See at CRAN