triplot: Explaining Correlated Features in Machine Learning Models
Tools for exploring effects of correlated features in predictive
models. The predict_triplot() function delivers instance-level explanations
that calculate the importance of the groups of explanatory variables. The
model_triplot() function delivers data-level explanations. The generic plot
function visualises in a concise way importance of hierarchical groups of
predictors. All of the the tools are model agnostic, therefore works for any
predictive machine learning models. Find more details in Biecek (2018)
<doi:10.48550/arXiv.1806.08915>.
Version: |
1.3.0 |
Depends: |
R (≥ 3.6) |
Imports: |
ggplot2, DALEX (≥ 1.3), glmnet, ggdendro, patchwork |
Suggests: |
testthat, knitr, randomForest, mlbench, ranger, gbm, covr |
Published: |
2020-07-13 |
DOI: |
10.32614/CRAN.package.triplot |
Author: |
Katarzyna Pekala [aut, cre],
Przemyslaw Biecek
[aut] |
Maintainer: |
Katarzyna Pekala <katarzyna.pekala at gmail.com> |
BugReports: |
https://github.com/ModelOriented/triplot/issues |
License: |
GPL-3 |
URL: |
https://github.com/ModelOriented/triplot |
NeedsCompilation: |
no |
Language: |
en-US |
Materials: |
NEWS |
CRAN checks: |
triplot results |
Documentation:
Downloads:
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