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) .


Reference manual

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1.3.0 by Katarzyna Pekala, a year ago


Report a bug at https://github.com/ModelOriented/triplot/issues

Browse source code at https://github.com/cran/triplot

Authors: Katarzyna Pekala [aut, cre] , Przemyslaw Biecek [aut]

Documentation:   PDF Manual  

GPL-3 license

Imports ggplot2, DALEX, glmnet, ggdendro, patchwork

Suggests testthat, knitr, randomForest, mlbench, ranger, gbm, covr

See at CRAN