Package: vip 0.4.1
Brandon M. Greenwell
vip: Variable Importance Plots
A general framework for constructing variable importance plots from various types of machine learning models in R. Aside from some standard model- specific variable importance measures, this package also provides model- agnostic approaches that can be applied to any supervised learning algorithm. These include 1) an efficient permutation-based variable importance measure, 2) variable importance based on Shapley values (Strumbelj and Kononenko, 2014) <doi:10.1007/s10115-013-0679-x>, and 3) the variance-based approach described in Greenwell et al. (2018) <arxiv:1805.04755>. A variance-based method for quantifying the relative strength of interaction effects is also included (see the previous reference for details).
Authors:
vip_0.4.1.tar.gz
vip_0.4.1.zip(r-4.5)vip_0.4.1.zip(r-4.4)vip_0.4.1.zip(r-4.3)
vip_0.4.1.tgz(r-4.4-any)vip_0.4.1.tgz(r-4.3-any)
vip_0.4.1.tar.gz(r-4.5-noble)vip_0.4.1.tar.gz(r-4.4-noble)
vip_0.4.1.tgz(r-4.4-emscripten)vip_0.4.1.tgz(r-4.3-emscripten)
vip.pdf |vip.html✨
vip/json (API)
NEWS
| # Install 'vip' in R: |
| install.packages('vip', repos = c('https://koalaverse.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/koalaverse/vip/issues
- titanic - Survival of Titanic passengers
- titanic_mice - Survival of Titanic passengers
interaction-effectmachine-learningpartial-dependence-plotsupervised-learning-algorithmsvariable-importancevariable-importance-plots
Last updated 1 years agofrom:648106b834. Checks:OK: 3 NOTE: 4. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 10 2024 |
| R-4.5-win | NOTE | Nov 10 2024 |
| R-4.5-linux | NOTE | Nov 10 2024 |
| R-4.4-win | NOTE | Nov 10 2024 |
| R-4.4-mac | NOTE | Nov 10 2024 |
| R-4.3-win | OK | Nov 10 2024 |
| R-4.3-mac | OK | Nov 10 2024 |
Exports:gen_friedmanlist_metricsvivi_firmvi_modelvi_permutevi_shapvip
Dependencies:clicodetoolscolorspacedplyrfansifarverforeachgenericsggplot2gluegtablehardhatisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerrlangscalestibbletidyselectutf8vctrsviridisLitewithryardstick
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Friedman benchmark data | gen_friedman |
| List metrics | list_metrics |
| Survival of Titanic passengers | titanic |
| Survival of Titanic passengers | titanic_mice |
| Variable importance | vi vi.default |
| Variance-based variable importance | vi_firm vi_firm.default |
| Model-specific variable importance | vi_model vi_model.C5.0 vi_model.cforest vi_model.constparty vi_model.cubist vi_model.cv.glmnet vi_model.default vi_model.earth vi_model.gbm vi_model.glmnet vi_model.H2OBinomialModel vi_model.H2OMultinomialModel vi_model.H2ORegressionModel vi_model.Learner vi_model.lgb.Booster vi_model.lm vi_model.mixo_pls vi_model.mixo_spls vi_model.mlp vi_model.ml_model_decision_tree_classification vi_model.ml_model_decision_tree_regression vi_model.ml_model_gbt_classification vi_model.ml_model_gbt_regression vi_model.ml_model_generalized_linear_regression vi_model.ml_model_linear_regression vi_model.ml_model_random_forest_classification vi_model.ml_model_random_forest_regression vi_model.model_fit vi_model.mvr vi_model.nn vi_model.nnet vi_model.RandomForest vi_model.randomForest vi_model.ranger vi_model.rpart vi_model.train vi_model.workflow vi_model.WrappedModel vi_model.xgb.Booster |
| Permutation-based variable importance | vi_permute vi_permute.default |
| SHAP-based variable importance | vi_shap vi_shap.default |
| Variable importance plots | vip vip.default vip.Learner vip.model_fit vip.workflow vip.WrappedModel |
