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]

triplot_1.3.1.tar.gz
triplot_1.3.1.zip(r-4.5)triplot_1.3.1.zip(r-4.4)triplot_1.3.1.zip(r-4.3)
triplot_1.3.1.tgz(r-4.4-any)triplot_1.3.1.tgz(r-4.3-any)
triplot_1.3.1.tar.gz(r-4.5-noble)triplot_1.3.1.tar.gz(r-4.4-noble)
triplot_1.3.1.tgz(r-4.4-emscripten)triplot_1.3.1.tgz(r-4.3-emscripten)
triplot.pdf |triplot.html
triplot/json (API)
NEWS

# Install 'triplot' in R:
install.packages('triplot', repos = c('https://modeloriented.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

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

On CRAN:

explanationsexplanatory-model-analysismachine-learningmodel-visualizationxai

3.65 score 9 stars 7 scripts 150 downloads 1 mentions 12 exports 43 dependencies

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

TargetResultDate
Doc / VignettesOKNov 21 2024
R-4.5-winOKNov 21 2024
R-4.5-linuxOKNov 21 2024
R-4.4-winOKNov 21 2024
R-4.4-macOKNov 21 2024
R-4.3-winOKNov 21 2024
R-4.3-macOKNov 21 2024

Exports:aspect_importanceaspect_importance_singlecalculate_triplotcluster_variablesget_samplegroup_variableshierarchical_importancelimelist_variablesmodel_triplotpredict_aspectspredict_triplot

Dependencies:clicodetoolscolorspaceDALEXfansifarverforeachggdendroggplot2glmnetgluegridExtragtableiBreakDowningredientsisobanditeratorskernelshaplabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepatchworkpillarpkgconfigR6RColorBrewerRcppRcppEigenrlangscalesshapesurvivaltibbleutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Calculates importance of variable groups (called aspects) for a selected observationaspect_importance aspect_importance.default aspect_importance.explainer lime predict_aspects
Aspects importance for single aspectsaspect_importance_single aspect_importance_single.default aspect_importance_single.explainer
Calculate triplot that sums up automatic aspect/feature importance groupingcalculate_triplot calculate_triplot.default calculate_triplot.explainer model_triplot predict_triplot print.triplot
Creates a cluster tree from numeric featurescluster_variables cluster_variables.default
Function for getting binary matrixget_sample
Helper function that combines clustering variables and creating aspect listgroup_variables
Calculates importance of hierarchically grouped aspectshierarchical_importance plot.hierarchical_importance
Cuts tree at custom height and returns a listlist_variables
Function for plotting aspect_importance resultsplot.aspect_importance
Plots tree with correlation valuesplot.cluster_variables
Plots triplotplot.triplot
Function for printing aspect_importance resultsprint.aspect_importance