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Model Agnostic Local Attributions

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Overview

The 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. DALEX, modelStudio, arenar.

Methodology behind the iBreakDown package is described in the arXiv paper and Explanatory Model Analysis book. It is a part of DrWhy.AI universe.

Installation

# 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")

Learn more

Find more examples in the EMA book: https://ema.drwhy.ai/.

This version also works with D3: see an example and demo.

plotD3

Acknowledgments

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

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Break Down with interactions for local explanations (SHAP, BreakDown, iBreakDown)

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