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, theme_drwhy() with a 'ggplot2' skin for all plots, generic print() and plot() for better usability of selected explainers. 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")

0.3.3 by Przemyslaw Biecek, 4 months ago


https://ModelOriented.github.io/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 [ctb]


Documentation:   PDF Manual  


GPL license


Imports DALEX, ggplot2, scales

Suggests gbm, gower, randomForest, titanic, xgboost, testthat, dplyr, r2d3, ggpubr, jsonlite


Imported by drifter, localModel, vivo.

Suggested by DALEX.


See at CRAN