Shed light on black box machine learning models by
the help of model performance, permutation variable importance (Fisher
et al. (2018) <arxiv:1801.01489>), 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, overall interaction strength, and variable contribution
breakdown 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.
stats, utils, dplyr, tidyr, rlang, ggplot2, ggpubr,
MetricsWeighted (>= 0.3.0)

Shed light on black box machine learning models by
the help of model performance, permutation variable importance (Fisher
et al. (2018) <arxiv:1801.01489>), 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, 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.

Shed light on black box machine learning models by
the help of model performance, variable importance, 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.

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.
stats, utils, dplyr, tidyr, rlang, rpart, rpart.plot, ggplot2,
ggpubr, MetricsWeighted (>= 0.3.0)