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.


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install.packages("flashlight")

0.7.3 by Michael Mayer, 3 months ago


https://github.com/mayer79/flashlight


Report a bug at https://github.com/mayer79/flashlight/issues


Browse source code at https://github.com/cran/flashlight


Authors: Michael Mayer [aut, cre, cph]


Documentation:   PDF Manual  


GPL (>= 2) license


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

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


See at CRAN