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) <doi:10.1214/aos/1013203451>), accumulated local effects (Apley D. W. (2016) <arXiv:1612.08468>), further effects plots, scatter plots, interaction strength, and variable contribution breakdown (approximate SHAP) for single observations (Gosiewska and Biecek (2019) <arxiv:1903.11420>). All tools are implemented to work with case weights and allow for stratified analysis. Furthermore, multiple flashlights can be combined and analyzed together.
|License||GPL (>= 2)|
|Package repository||View on GitHub|
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