Shed Light on Black Box Machine Learning Models

Shed light on black box machine learning models by the help of model performance, variable importance, global surrogate models, ICE profiles, partial dependence (Friedman J. H. (2001) ), accumulated local effects (Apley D. W. (2016) ), further effects plots, scatter plots, interaction strength, and variable contribution breakdown (approximate SHAP) for single observations (Gosiewska and Biecek (2019) ). All tools are implemented to work with case weights and allow for stratified analysis. Furthermore, multiple flashlights can be combined and analyzed together.


Reference manual

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0.8.0 by Michael Mayer, 9 months ago

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Browse source code at

Authors: Michael Mayer [aut, cre, cph]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports dplyr, cowplot, ggplot2, MetricsWeighted, rpart, rpart.plot, stats, tidyr, tidyselect, utils, withr

Suggests knitr, caret, mlr3, mlr3learners, moderndive, ranger, rmarkdown, testthat, xgboost

Imported by vivid.

See at CRAN