@article{RJ-2020-013,
  author = {Brandon M. Greenwell and Bradley C. Boehmke},
  title = {{Variable Importance Plots—An Introduction to the vip Package}},
  year = {2020},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2020-013},
  url = {https://doi.org/10.32614/RJ-2020-013},
  pages = {343--366},
  volume = {12},
  number = {1}
}

@article{bring-1994-standardize,
    author = {Johan Bring},
    title = {How to Standardize Regression Coefficients},
    journal = {The American Statistician},
    volume = {48},
    number = {3},
    pages = {209-213},
    year  = {1994},
    publisher = {Taylor \& Francis},
    url = {https://www.tandfonline.com/doi/abs/10.1080/00031305.1994.10476059}
}

@Article{R-randomForest,
    title = {Classification and Regression by randomForest},
    author = {Andy Liaw and Matthew Wiener},
    journal = {R News},
    year = {2002},
    volume = {2},
    number = {3},
    pages = {18-22},
    url = {https://CRAN.R-project.org/doc/Rnews/},
  }
@Manual{R-yardstick,
    title = {yardstick: Tidy Characterizations of Model Performance},
    author = {Max Kuhn and Davis Vaughan and Emil Hvitfeldt},
    year = {2023},
    note = {R package version 1.2.0},
    url = {https://CRAN.R-project.org/package=yardstick},
  }
@Manual{R-ALEPlot,
  title = {ALEPlot: Accumulated Local Effects (ALE) Plots and Partial Dependence
(PD) Plots},
  author = {Dan Apley},
  year = {2018},
  note = {R package version 1.1},
  url = {https://CRAN.R-project.org/package=ALEPlot},
}
@Manual{R-AmesHousing,
  title = {AmesHousing: The Ames Iowa Housing Data},
  author = {Max Kuhn},
  year = {2017},
  note = {R package version 0.0.3},
  url = {https://CRAN.R-project.org/package=AmesHousing},
}
@Manual{R-caret,
  title = {caret: Classification and Regression Training},
  author = {Max Kuhn},
  year = {2020},
  note = {R package version 6.0-85},
  url = {https://CRAN.R-project.org/package=caret},
}
@Manual{R-DALEX,
  title = {DALEX: Descriptive mAchine Learning EXplanations},
  author = {Przemyslaw Biecek},
  year = {2019},
  note = {R package version 0.4.9},
  url = {https://CRAN.R-project.org/package=DALEX},
}
@Manual{R-data.table,
  title = {data.table: Extension of `data.frame`},
  author = {Matt Dowle and Arun Srinivasan},
  year = {2019},
  note = {R package version 1.12.8},
  url = {https://CRAN.R-project.org/package=data.table},
}
@Manual{R-doParallel,
  title = {doParallel: Foreach Parallel Adaptor for the 'parallel' Package},
  author = {Microsoft Corporation and Steve Weston},
  year = {2019},
  note = {R package version 1.0.15},
  url = {https://CRAN.R-project.org/package=doParallel},
}
@Manual{R-DT,
  title = {DT: A Wrapper of the JavaScript Library 'DataTables'},
  author = {Yihui Xie and Joe Cheng and Xianying Tan},
  year = {2019},
  note = {R package version 0.11},
  url = {https://CRAN.R-project.org/package=DT},
}
@Manual{R-earth,
  title = {earth: Multivariate Adaptive Regression Splines},
  author = {Stephen Milborrow. Derived from mda:mars by Trevor Hastie and Rob Tibshirani. Uses Alan Miller's Fortran utilities with Thomas Lumley's leaps wrapper.},
  year = {2019},
  note = {R package version 5.1.2},
  url = {https://CRAN.R-project.org/package=earth},
}
@Manual{R-fastshap,
  title = {fastshap: Fast Approximate Shapley Values},
  author = {Brandon Greenwell},
  note = {R package version 0.0.3.9000},
  url = {https://github.com/bgreenwell/fastshap},
  year = {2019},
}
@Manual{R-foreach,
  title = {foreach: Provides Foreach Looping Construct},
  author = {{Revolution Analytics} and Steve Weston}},
  year = {2019},
  note = {R package version 1.4.7},
  url = {https://CRAN.R-project.org/package=foreach},
}
@Manual{R-gbm,
  title = {gbm: Generalized Boosted Regression Models},
  author = {Brandon Greenwell and Bradley Boehmke and Jay Cunningham and GBM Developers},
  year = {2019},
  note = {R package version 2.1.5},
  url = {https://CRAN.R-project.org/package=gbm},
}
@Manual{R-ggplot2,
  title = {ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics},
  author = {Hadley Wickham and Winston Chang and Lionel Henry and Thomas Lin Pedersen and Kohske Takahashi and Claus Wilke and Kara Woo and Hiroaki Yutani},
  year = {2019},
  note = {R package version 3.2.1},
  url = {https://CRAN.R-project.org/package=ggplot2},
}
@Manual{R-glmnet,
  title = {glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models},
  author = {Jerome Friedman and Trevor Hastie and Rob Tibshirani and Balasubramanian Narasimhan and Noah Simon},
  year = {2019},
  note = {R package version 3.0-2},
  url = {https://CRAN.R-project.org/package=glmnet},
}
@Manual{R-iBreakDown,
  title = {iBreakDown: Model Agnostic Instance Level Variable Attributions},
  author = {Przemyslaw Biecek and Alicja Gosiewska and Hubert Baniecki and Adam Izdebski},
  year = {2019},
  note = {R package version 0.9.9},
  url = {https://CRAN.R-project.org/package=iBreakDown},
}
@Manual{R-iml,
  title = {iml: Interpretable Machine Learning},
  author = {Christoph Molnar},
  year = {2019},
  note = {R package version 0.9.0},
  url = {https://CRAN.R-project.org/package=iml},
}
@Manual{R-ingredients,
  title = {ingredients: Effects and Importances of Model Ingredients},
  author = {Przemyslaw Biecek and Hubert Baniecki and Adam Izdebski},
  year = {2019},
  note = {R package version 0.5.0},
  url = {https://CRAN.R-project.org/package=ingredients},
}
@Manual{R-kernlab,
  title = {kernlab: Kernel-Based Machine Learning Lab},
  author = {Alexandros Karatzoglou and Alex Smola and Kurt Hornik},
  year = {2019},
  note = {R package version 0.9-29},
  url = {https://CRAN.R-project.org/package=kernlab},
}
@Manual{R-measures,
  title = {measures: Performance Measures for Statistical Learning},
  author = {Philipp Probst},
  year = {2018},
  note = {R package version 0.2},
  url = {https://CRAN.R-project.org/package=measures},
}
@Manual{R-microbenchmark,
  title = {microbenchmark: Accurate Timing Functions},
  author = {Olaf Mersmann},
  year = {2019},
  note = {R package version 1.4-7},
  url = {https://CRAN.R-project.org/package=microbenchmark},
}
@Manual{R-mlr,
  title = {mlr: Machine Learning in R},
  author = {Bernd Bischl and Michel Lang and Lars Kotthoff and Julia Schiffner and Jakob Richter and Zachary Jones and Giuseppe Casalicchio and Mason Gallo and Patrick Schratz},
  year = {2020},
  note = {R package version 2.17.0},
  url = {https://CRAN.R-project.org/package=mlr},
}
@Manual{R-mlr3,
  title = {mlr3: Machine Learning in R - Next Generation},
  author = {Michel Lang and Bernd Bischl and Jakob Richter and Patrick Schratz and Martin Binder},
  year = {2019},
  note = {R package version 0.1.6},
  url = {https://CRAN.R-project.org/package=mlr3},
}
@Manual{R-mmpf,
  title = {mmpf: Monte-Carlo Methods for Prediction Functions},
  author = {Zachary Jones},
  year = {2018},
  note = {R package version 0.0.5},
  url = {https://CRAN.R-project.org/package=mmpf},
}
@Manual{R-nnet,
  title = {nnet: Feed-Forward Neural Networks and Multinomial Log-Linear Models},
  author = {Brian Ripley},
  year = {2016},
  note = {R package version 7.3-12},
  url = {https://CRAN.R-project.org/package=nnet},
}
@Manual{R-party,
  title = {party: A Laboratory for Recursive Partytioning},
  author = {Torsten Hothorn and Kurt Hornik and Carolin Strobl and Achim Zeileis},
  year = {2019},
  note = {R package version 1.3-3},
  url = {https://CRAN.R-project.org/package=party},
}
@Manual{R-partykit,
  title = {partykit: A Toolkit for Recursive Partytioning},
  author = {Torsten Hothorn and Achim Zeileis},
  year = {2019},
  note = {R package version 1.2-5},
  url = {https://CRAN.R-project.org/package=partykit},
}
@Manual{R-pdp,
  title = {pdp: Partial Dependence Plots},
  author = {Brandon Greenwell},
  note = {https://bgreenwell.github.io/pdp/index.html,
https://github.com/bgreenwell/pdp},
  year = {2019},
}
@Manual{R-pkgsearch,
  title = {pkgsearch: Search and Query CRAN R Packages},
  author = {Gábor Csárdi and Maëlle Salmon},
  year = {2019},
  note = {R package version 3.0.2},
  url = {https://CRAN.R-project.org/package=pkgsearch},
}
@Manual{R-plyr,
  title = {plyr: Tools for Splitting, Applying and Combining Data},
  author = {Hadley Wickham},
  year = {2019},
  note = {R package version 1.8.5},
  url = {https://CRAN.R-project.org/package=plyr},
}
@Manual{R-R6,
  title = {R6: Encapsulated Classes with Reference Semantics},
  author = {Winston Chang},
  year = {2019},
  note = {R package version 2.4.1},
  url = {https://CRAN.R-project.org/package=R6},
}
@Manual{R-randomForestExplainer,
  title = {randomForestExplainer: Explaining and Visualizing Random Forests in Terms of Variable
Importance},
  author = {Aleksandra Paluszynska and Przemyslaw Biecek and Yue Jiang},
  year = {2019},
  note = {R package version 0.10.0},
  url = {https://CRAN.R-project.org/package=randomForestExplainer},
}
@Manual{R-ranger,
  title = {ranger: A Fast Implementation of Random Forests},
  author = {Marvin N. Wright and Stefan Wager and Philipp Probst},
  year = {2020},
  note = {R package version 0.12.1},
  url = {https://CRAN.R-project.org/package=ranger},
}
@Manual{R-rfVarImpOOB,
  title = {rfVarImpOOB: Unbiased Variable Importance for Random Forests},
  author = {Markus Loecher},
  year = {2019},
  note = {R package version 1.0},
  url = {https://CRAN.R-project.org/package=rfVarImpOOB},
}
@Manual{R-SuperLearner,
  title = {SuperLearner: Super Learner Prediction},
  author = {Eric Polley and Erin LeDell and Chris Kennedy and Mark {van der Laan}},
  year = {2019},
  note = {R package version 2.0-26},
  url = {https://CRAN.R-project.org/package=SuperLearner},
}
@Manual{R-tibble,
  title = {tibble: Simple Data Frames},
  author = {Kirill Müller and Hadley Wickham},
  year = {2019},
  note = {R package version 2.1.3},
  url = {https://CRAN.R-project.org/package=tibble},
}
@Manual{R-tree.interpreter,
  title = {tree.interpreter: Random Forest Prediction Decomposition and Feature Importance
Measure},
  author = {Qingyao Sun},
  year = {2019},
  note = {R package version 0.1.0},
  url = {https://CRAN.R-project.org/package=tree.interpreter},
}
@Manual{R-varImp,
  title = {varImp: RF Variable Importance for Arbitrary Measures},
  author = {Philipp Probst},
  year = {2019},
  note = {R package version 0.3},
  url = {https://CRAN.R-project.org/package=varImp},
}
@Manual{R-vip,
  title = {vip: Variable Importance Plots},
  author = {Brandon Greenwell and Brad Boehmke and Bernie Gray},
  note = {R package version 0.2.1},
  url = {https://github.com/koalaverse/vip/},
  year = {2019},
}
@Manual{R-vita,
  title = {vita: Variable Importance Testing Approaches},
  author = {Ender Celik},
  year = {2015},
  note = {R package version 1.0.0},
  url = {https://CRAN.R-project.org/package=vita},
}
@Manual{R-vivo,
  title = {vivo: Local Variable Importance via Oscillations of Ceteris Paribus
Profiles},
  author = {Anna Kozak and Przemyslaw Biecek},
  year = {2019},
  note = {R package version 0.1.1},
  url = {https://CRAN.R-project.org/package=vivo},
}
@Manual{R-xgboost,
  title = {xgboost: Extreme Gradient Boosting},
  author = {Tianqi Chen and Tong He and Michael Benesty and Vadim Khotilovich and Yuan Tang and Hyunsu Cho and Kailong Chen and Rory Mitchell and Ignacio Cano and Tianyi Zhou and Mu Li and Junyuan Xie and Min Lin and Yifeng Geng and Yutian Li},
  year = {2019},
  note = {R package version 0.90.0.2},
  url = {https://CRAN.R-project.org/package=xgboost},
}
@Article{DALEX2018,
  title = {DALEX: Explainers for Complex Predictive Models in R},
  author = {Przemyslaw Biecek},
  journal = {Journal of Machine Learning Research},
  year = {2018},
  volume = {19},
  pages = {1-5},
  number = {84},
  url = {https://jmlr.org/papers/v19/18-416.html},
}
@Book{ggplot22016,
  author = {Hadley Wickham},
  title = {ggplot2: Elegant Graphics for Data Analysis},
  publisher = {Springer-Verlag New York},
  year = {2016},
  isbn = {978-3-319-24277-4},
  url = {https://ggplot2.tidyverse.org},
}
@Article{glmnet2010,
  title = {Regularization Paths for Generalized Linear Models via Coordinate Descent},
  author = {Jerome Friedman and Trevor Hastie and Robert Tibshirani},
  journal = {Journal of Statistical Software},
  year = {2010},
  volume = {33},
  number = {1},
  pages = {1--22},
  url = {https://www.jstatsoft.org/v33/i01/},
}
@Article{glmnet2011,
  title = {Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent},
  author = {Noah Simon and Jerome Friedman and Trevor Hastie and Rob Tibshirani},
  journal = {Journal of Statistical Software},
  year = {2011},
  volume = {39},
  number = {5},
  pages = {1--13},
  url = {https://www.jstatsoft.org/v39/i05/},
}
@Misc{iBreakDown2019,
  title = {iBreakDown: Uncertainty of Model Explanations for Non-additive Predictive Models},
  author = {Alicja Gosiewska and Przemyslaw Biecek},
  year = {2019},
  eprint = {arXiv:1903.11420},
  url = {https://arxiv.org/abs/1903.11420v1},
}
@Article{iml2018,
  author = {Christoph Molnar and Bernd Bischl and Giuseppe Casalicchio},
  title = {iml: An R package for Interpretable Machine Learning},
  doi = {10.21105/joss.00786},
  url = {https://joss.theoj.org/papers/10.21105/joss.00786},
  year = {2018},
  publisher = {Journal of Open Source Software},
  volume = {3},
  number = {26},
  pages = {786},
  journal = {JOSS},
}
@Article{kernlab2004,
  title = {kernlab -- An {S4} Package for Kernel Methods in {R}},
  author = {Alexandros Karatzoglou and Alex Smola and Kurt Hornik and Achim Zeileis},
  journal = {Journal of Statistical Software},
  year = {2004},
  volume = {11},
  number = {9},
  pages = {1--20},
  url = {https://www.jstatsoft.org/v11/i09/},
}
@Article{mlr,
  title = {{mlr}: Machine Learning in R},
  author = {Bernd Bischl and Michel Lang and Lars Kotthoff and Julia Schiffner and Jakob Richter and Erich Studerus and Giuseppe Casalicchio and Zachary M. Jones},
  journal = {Journal of Machine Learning Research},
  year = {2016},
  volume = {17},
  number = {170},
  pages = {1-5},
  url = {https://jmlr.org/papers/v17/15-066.html},
}
@Article{automatic,
  title = {Automatic model selection for high-dimensional survival analysis},
  author = {Michel Lang and Helena Kotthaus and Peter Marwedel and Claus Weihs and Joerg Rahnenfuehrer and Bernd Bischl},
  journal = {Journal of Statistical Computation and Simulation},
  year = {2014},
  volume = {85},
  number = {1},
  pages = {62-76},
  publisher = {Taylor \& Francis},
}
@InCollection{bischl2016class,
  title = {On Class Imbalance Correction for Classification Algorithms in Credit Scoring},
  author = {Bernd Bischl and Tobias Kuehn and Gero Szepannek},
  booktitle = {Operations Research Proceedings 2014},
  pages = {37-43},
  year = {2016},
  publisher = {Springer},
}
@Article{mlrmbo,
  title = {mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions},
  author = {Bernd Bischl and Jakob Richter and Jakob Bossek and Daniel Horn and Janek Thomas and Michel Lang},
  journal = {arXiv preprint arXiv:1703.03373},
  year = {2017},
}
@Article{multilabel,
  title = {Multilabel Classification with R Package mlr},
  author = {Philipp Probst and Quay Au and Giuseppe Casalicchio and Clemens Stachl and Bernd Bischl},
  journal = {arXiv preprint arXiv:1703.08991},
  year = {2017},
}
@Article{openml,
  title = {OpenML: An R package to connect to the machine learning platform OpenML},
  author = {Giuseppe Casalicchio and Jakob Bossek and Michel Lang and Dominik Kirchhoff and Pascal Kerschke and Benjamin Hofner and Heidi Seibold and Joaquin Vanschoren and Bernd Bischl},
  journal = {Computational Statistics},
  pages = {1-15},
  year = {2017},
  publisher = {Springer},
}
@Article{mlr3,
  title = {{mlr3}: A modern object-oriented machine learning framework in {R}},
  author = {Michel Lang and Martin Binder and Jakob Richter and Patrick Schratz and Florian Pfisterer and Stefan Coors and Quay Au and Giuseppe Casalicchio and Lars Kotthoff and Bernd Bischl},
  journal = {Journal of Open Source Software},
  year = {2019},
  month = {dec},
  doi = {10.21105/joss.01903},
  url = {https://joss.theoj.org/papers/10.21105/joss.01903},
}
@Book{nnet2002,
  title = {Modern Applied Statistics with S},
  author = {W. N. Venables and B. D. Ripley},
  publisher = {Springer},
  edition = {Fourth},
  address = {New York},
  year = {2002},
  note = {ISBN 0-387-95457-0},
  url = {https://www.stats.ox.ac.uk/pub/MASS4},
}
@Article{party2006a,
  title = {Unbiased Recursive Partitioning: A Conditional Inference Framework},
  author = {Torsten Hothorn and Kurt Hornik and Achim Zeileis},
  journal = {Journal of Computational and Graphical Statistics},
  year = {2006},
  volume = {15},
  number = {3},
  pages = {651--674},
}
@Article{party2008a,
  title = {Model-Based Recursive Partitioning},
  author = {Achim Zeileis and Torsten Hothorn and Kurt Hornik},
  journal = {Journal of Computational and Graphical Statistics},
  year = {2008},
  volume = {17},
  number = {2},
  pages = {492--514},
}
@Article{party2006b,
  title = {Survival Ensembles},
  author = {Torsten Hothorn and Peter Buehlmann and Sandrine Dudoit and Annette Molinaro and Mark {Van Der Laan}},
  journal = {Biostatistics},
  year = {2006},
  volume = {7},
  number = {3},
  pages = {355--373},
}
@Article{party2007a,
  title = {Bias in Random Forest Variable Importance Measures: Illustrations, Sources and a Solution},
  author = {Carolin Strobl and Anne-Laure Boulesteix and Achim Zeileis and Torsten Hothorn},
  journal = {BMC Bioinformatics},
  year = {2007},
  volume = {8},
  number = {25},
  url = {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-8-25},
}
@Article{party2008b,
  title = {Conditional Variable Importance for Random Forests},
  author = {Carolin Strobl and Anne-Laure Boulesteix and Thomas Kneib and Thomas Augustin and Achim Zeileis},
  journal = {BMC Bioinformatics},
  year = {2008},
  volume = {9},
  number = {307},
  url = {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-307},
}
@Article{partykit2015,
  title = {{partykit}: A Modular Toolkit for Recursive Partytioning in {R}},
  author = {Torsten Hothorn and Achim Zeileis},
  journal = {Journal of Machine Learning Research},
  year = {2015},
  volume = {16},
  pages = {3905-3909},
  url = {https://jmlr.org/papers/v16/hothorn15a.html},
}
@Article{partykit2006,
  title = {Unbiased Recursive Partitioning: A Conditional Inference Framework},
  author = {Torsten Hothorn and Kurt Hornik and Achim Zeileis},
  journal = {Journal of Computational and Graphical Statistics},
  year = {2006},
  volume = {15},
  number = {3},
  doi = {10.1198/106186006X133933},
  pages = {651--674},
}
@Article{partykit2008,
  title = {Model-Based Recursive Partitioning},
  author = {Achim Zeileis and Torsten Hothorn and Kurt Hornik},
  journal = {Journal of Computational and Graphical Statistics},
  year = {2008},
  volume = {17},
  number = {2},
  doi = {10.1198/106186008X319331},
  pages = {492--514},
}
@Article{pdp2017,
  title = {pdp: An R Package for Constructing Partial Dependence Plots},
  author = {Brandon M. Greenwell},
  journal = {The R Journal},
  year = {2017},
  volume = {9},
  number = {1},
  pages = {421--436},
  url = {https://journal.r-project.org/archive/2017/RJ-2017-016/index.html},
}
@Article{plyr2011,
  title = {The Split-Apply-Combine Strategy for Data Analysis},
  author = {Hadley Wickham},
  journal = {Journal of Statistical Software},
  year = {2011},
  volume = {40},
  number = {1},
  pages = {1--29},
  url = {https://www.jstatsoft.org/v40/i01/},
}
@Article{ranger2017,
  title = {{ranger}: A Fast Implementation of Random Forests for High Dimensional Data in {C++} and {R}},
  author = {Marvin N. Wright and Andreas Ziegler},
  journal = {Journal of Statistical Software},
  year = {2017},
  volume = {77},
  number = {1},
  pages = {1--17},
  doi = {10.18637/jss.v077.i01},
}
@Article{tree.interpreter2019,
  title = {A {Debiased} {MDI} {Feature} {Importance} {Measure} for {Random} {Forests}},
  url = {https://arxiv.org/abs/1906.10845},
  abstract = {Tree ensembles such as Random Forests have achieved impressive empirical success across a wide variety of applications. To understand how these models make predictions, people routinely turn to feature importance measures calculated from tree ensembles. It has long been known that Mean Decrease Impurity (MDI), one of the most widely used measures of feature importance, incorrectly assigns high importance to noisy features, leading to systematic bias in feature selection. In this paper, we address the feature selection bias of MDI from both theoretical and methodological perspectives. Based on the original definition of MDI by Breiman et al. for a single tree, we derive a tight non-asymptotic bound on the expected bias of MDI importance of noisy features, showing that deep trees have higher (expected) feature selection bias than shallow ones. However, it is not clear how to reduce the bias of MDI using its existing analytical expression. We derive a new analytical expression for MDI, and based on this new expression, we are able to propose a debiased MDI feature importance measure using out-of-bag samples, called MDI-oob. For both the simulated data and a genomic ChIP dataset, MDI-oob achieves state-of-the-art performance in feature selection from Random Forests for both deep and shallow trees.},
  urldate = {2019-10-18},
  journal = {arXiv:1906.10845 [cs, stat]},
  author = {Xiao Li and Yu Wang and Sumanta Basu and Karl Kumbier and Bin Yu},
  month = {jun},
  year = {2019},
  note = {arXiv: 1906.10845},
  keywords = {Statistics - Machine Learning, Computer Science - Machine Learning},
  annote = {Comment: The first two authors contributed equally to this paper},
}
@incollection{lundberg_unified_2017,
  title = {A Unified Approach to Interpreting Model Predictions},
  author = {Lundberg, Scott M and Lee, Su-In},
  booktitle = {Advances in Neural Information Processing Systems 30},
  pages = {4765--4774},
  year = {2017},
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