Interpretability methods to analyze the behavior
and predictions of any machine learning model. Implemented methods
are: Feature importance described by Fisher et al. (2018)
<arXiv:1801.01489>, accumulated local effects plots described by Apley
(2018) <arXiv:1612.08468>, partial dependence plots described by
Friedman (2001) <www.jstor.org/stable/2699986>, individual
conditional expectation ('ice') plots described by Goldstein et al.
(2013) <doi:10.1080/10618600.2014.907095>, local models (variant of
'lime') described by Ribeiro et. al (2016) <arXiv:1602.04938>, the
Shapley Value described by Strumbelj et. al (2014)
<doi:10.1007/s10115-013-0679-x>, feature interactions described by
Friedman et. al <doi:10.1214/07-AOAS148> and tree surrogate models.
Tests Vignettes
Available Snapshots
This version of iml can be found in the following snapshots:
