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Model ingredients - model level feature effects and feature importance
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README.md

Model Ingredients

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Overview

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/PM_VEE/ceterisParibus.html),
  • partial_dependency() for Partial Dependency Plots,
  • conditional_dependency() for Conditional Dependency Plots also called M Plots,
  • accumulated_dependency() for Accumulated Local Effects Plots,
  • aggregate_profiles() and cluster_profiles() for aggregation of Ceteris Paribus Profiles,
  • aspect_importance() for LIME style explanations,
  • calculate_oscillations() for calculation of the Ceteris Paribus Oscillations (read more at https://pbiecek.github.io/PM_VEE/ceterisParibusOscillations.html),
  • ceteris_paribus_2d() for Ceteris Paribus 2D Profiles (read more at https://pbiecek.github.io/PM_VEE/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 Predictive Models: Explore, Explain, and Debug e-book. The ingredients package is a part of DrWhy.AI universe.

Installation

# 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")

Interactive plots with D3

feature_importance(), ceteris_paribus() and aggregated_profiles() also work with D3! see an example plotD3

Acknowledgments

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

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