The ingredients package is a collection of tools for assessment of feature importance and feature effects.

Key functions:

• feature_importance() for assessment of global level feature importance,
• ceteris_paribus() for calculation of the Ceteris Paribus / What-If Profiles (read more at https://pbiecek.github.io/ema/ceterisParibus.html),
• partial_dependence() for Partial Dependence Plots,
• conditional_dependence() for Conditional Dependence Plots also called M Plots,
• accumulated_dependence() for Accumulated Local Effects Plots,
• aggregate_profiles() and cluster_profiles() for aggregation of Ceteris Paribus Profiles,
• calculate_oscillations() for calculation of the Ceteris Paribus Oscillations (read more at https://pbiecek.github.io/ema/ceterisParibusOscillations.html),
• ceteris_paribus_2d() for Ceteris Paribus 2D Profiles (read more at https://pbiecek.github.io/ema/ceterisParibus2d.html),
• generic print() and plot() for better usability of selected explainers,
• generic plotD3() for interactive, D3 based explanations,
• generic describe() for explanations in natural language.

The philosophy behind ingredients explanations is described in the Explanatory Model Analysis. Explore, Explain and Examine Predictive Models e-book. The ingredients package is a part of DrWhy.AI universe.

# the easiest way to get ingredients is to install it from CRAN:
install.packages("ingredients")

# Or the the development version from GitHub:
# install.packages("devtools")
devtools::install_github("ModelOriented/ingredients")

feature_importance(), ceteris_paribus() and aggregated_profiles() also work with D3! see an example

Work on this package was financially supported by the 'NCN Opus grant 2016/21/B/ST6/02176'.