Package: triplot 1.3.1

Katarzyna Pekala

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) <arxiv:1806.08915>.

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

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Bug tracker:https://github.com/modeloriented/triplot/issues

On CRAN:

explanationsexplanatory-model-analysismachine-learningmodel-visualizationxai

12 exports 9 stars 1.64 score 43 dependencies 1 mentions 7 scripts 132 downloads

Last updated 4 years agofrom:a9721315b1. Checks:OK: 7. Indexed: yes.

Exports:aspect_importanceaspect_importance_singlecalculate_triplotcluster_variablesget_samplegroup_variableshierarchical_importancelimelist_variablesmodel_triplotpredict_aspectspredict_triplot

Dependencies:clicodetoolscolorspaceDALEXfansifarverforeachggdendroggplot2glmnetgluegridExtragtableiBreakDowningredientsisobanditeratorskernelshaplabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepatchworkpillarpkgconfigR6RColorBrewerRcppRcppEigenrlangscalesshapesurvivaltibbleutf8vctrsviridisLitewithr