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triplot  

Explaining Correlated Features in Machine Learning Models
View on CRAN: Click here


Download and install triplot package within the R console
Install from CRAN:
install.packages("triplot")

Install from Github:
library("remotes")
install_github("cran/triplot")

Install by package version:
library("remotes")
install_version("triplot", "1.3.0")



Attach the package and use:
library("triplot")
Maintained by
Katarzyna Pekala
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-06-09
Latest Update: 2020-07-13
Description:
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>.
How to cite:
Katarzyna Pekala (2020). triplot: Explaining Correlated Features in Machine Learning Models. R package version 1.3.0, https://cran.r-project.org/web/packages/triplot. Accessed 21 Nov. 2024.
Previous versions and publish date:
1.2.0 (2020-06-09 14:40)
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