Interpretability methods to analyze the behavior and
predictions of any machine learning model. Implemented methods are:
Feature importance described by Fisher et al. (2018)
<doi:10.48550/arxiv.1801.01489>, accumulated local effects plots described by Apley
(2018) <doi:10.48550/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) <doi:10.48550/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.
| Version: |
0.11.3 |
| Imports: |
checkmate, data.table, Formula, future, future.apply, ggplot2, Metrics, R6 |
| Suggests: |
ALEPlot, bench, bit64, caret, covr, e1071, future.callr, glmnet, gower, h2o, keras (≥ 2.2.5.0), knitr, MASS, mlr, mlr3, party, partykit, patchwork, randomForest, ranger, rmarkdown, rpart, testthat, yaImpute |
| Published: |
2024-04-27 |
| Author: |
Giuseppe Casalicchio [aut, cre],
Christoph Molnar [aut],
Patrick Schratz 
[aut] |
| Maintainer: |
Giuseppe Casalicchio <giuseppe.casalicchio at lmu.de> |
| BugReports: |
https://github.com/giuseppec/iml/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://giuseppec.github.io/iml/,
https://github.com/giuseppec/iml/ |
| NeedsCompilation: |
no |
| Citation: |
iml citation info |
| Materials: |
NEWS |
| In views: |
MachineLearning |
| CRAN checks: |
iml results |