iBreakDown package is a model agnostic tool for explanation of predictions from black boxes ML models.
Break Down Table shows contributions of every variable to a final prediction.
Break Down Plot presents variable contributions in a concise graphical way.
SHAP (Shapley Additive Attributions) values are calculated as average from random Break Down profiles.
This package works for binary classifiers as well as regression models.
iBreakDown is a successor of the breakDown package. It is faster (complexity
O(p) instead of
O(p^2)). It supports variable interactions and interactive explanations with D3.js visualizations. It is imported and used to compute model explanations in multiple packages e.g.
# the easiest way to get iBreakDown is to install it from CRAN: install.packages("iBreakDown") # Or the the development version from GitHub: # install.packages("devtools") devtools::install_github("ModelOriented/iBreakDown")
Find more examples in the EMA book: https://ema.drwhy.ai/.
Work on this package was financially supported by the
NCN Opus grant 2016/21/B/ST6/02176.
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