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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 |
| 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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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