Fetching the paper…
Reading the bibliography…
An increasing number of model-agnostic interpretation techniques for machine learning (ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) and Shapley values provide insightful model interpretations, but can lead to wrong conclusions if applied incorrectly.
Dunn, O.J.: Multiple comparisons among means. Journal of the American Statistical Association 56
1961
Earlier work this paper cites.
Kuhle, S., Maguire, B., Zhang, H., Hamilton, D., Allen, A.C., Joseph, K., Allen, V.M.: Comparison of logistic regression with machine learning methods for the prediction of fetal growth abnormalities: a retrospective cohort study. BMC Pregnancy and Childbirth 18
1971
Earlier work this paper cites.
Holm, S.: A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6
1979
Earlier work this paper cites.
Liebetrau, A.: Measures of Association. No. Bd. 32;Bd. 1983 in 07, SAGE Publications (1983)
1983
Earlier work this paper cites.
Breiman, L., Friedman, J.H.: Estimating optimal transformations for multiple regression and correlation. Journal of the American statistical Association 80
1985
Earlier work this paper cites.
Hastie, T., Tibshirani, R.: Generalized Additive Models. Statistical Science 1
1986
Earlier work this paper cites.
Friedman, J.H., et al.: Multivariate adaptive regression splines. The Annals of Statistics 19
1991
Earlier work this paper cites.
Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial intelligence 97
1997
Earlier work this paper cites.
Perneger, T.V.: What’s wrong with bonferroni adjustments. BMJ 316
1998
Earlier work this paper cites.
Breiman, L.: Random forests. Machine learning 45
2001
Earlier work this paper cites.
Kadir, T., Brady, M.: Saliency, scale and image description. International Journal of Computer Vision 45
2001
Earlier work this paper cites.
Bach, F.R., Jordan, M.I.: Kernel independent component analysis. Journal of Machine Learning Research 3
2002
Earlier work this paper cites.
Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., Vanthienen, J.: Benchmarking state-of-the-art classification algorithms for credit scoring. Journal of the Operational Research Society 54
2003
Earlier work this paper cites.
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of machine learning research 3
2003
Earlier work this paper cites.
Hooker, G.: Discovering additive structure in black box functions. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. p. 575–580. KDD ’04, Association for Computing Machinery, New York, NY, USA (2004). https://doi.org/10.1145/1014052.1014122
2004
Earlier work this paper cites.
Gretton, A., Bousquet, O., Smola, A., Schölkopf, B.: Measuring statistical dependence with hilbert-schmidt norms. In: International Conference on Algorithmic Learning Theory. pp. 63–77. Springer (2005). https://doi.org/10.1007/11564089_7
2005
Earlier work this paper cites.
Yuan, M., Lin, Y.: Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 68
2005
Earlier work this paper cites.
Hand, D.J.: Classifier Technology and the Illusion of Progress. Statistical Science 21
2006
Earlier work this paper cites.
Chakraborty, D., Pal, N.R.: Selecting useful groups of features in a connectionist framework. IEEE Transactions on Neural Networks 19
2007
Earlier work this paper cites.
Hooker, G.: Generalized functional anova diagnostics for high-dimensional functions of dependent variables. Journal of Computational and Graphical Statistics 16
2007
Earlier work this paper cites.
Simon, R.: Resampling strategies for model assessment and selection. In: Fundamentals of data mining in genomics and proteomics, pp. 173–186. Springer (2007). https://doi.org/10.1007/978-0-387-47509-7_8
2007
Earlier work this paper cites.
Székely, G.J., Rizzo, M.L., Bakirov, N.K., et al.: Measuring and testing dependence by correlation of distances. The Annals of Statistics 35
2007
Earlier work this paper cites.
Claeskens, G., Hjort, N.L., et al.: Model selection and model averaging. Cambridge Books (2008). https://doi.org/10.1017/CBO9780511790485
2008
Earlier work this paper cites.
Friedman, J.H., Popescu, B.E.: Predictive learning via rule ensembles. Annals of Applied Statistics 2
2008
Earlier work this paper cites.
Khamis, H.: Measures of association: how to choose? Journal of Diagnostic Medical Sonography 24
2008
Earlier work this paper cites.
Schmid, M., Hothorn, T.: Boosting additive models using component-wise p-splines. Computational Statistics & Data Analysis 53
2008
Earlier work this paper cites.
Strobl, C., Boulesteix, A.L., Kneib, T., Augustin, T., Zeileis, A.: Conditional variable importance for random forests. BMC bioinformatics 9
2008
Earlier work this paper cites.
Lozano, A.C., Abe, N., Liu, Y., Rosset, S.: Grouped graphical granger modeling for gene expression regulatory networks discovery. Bioinformatics 25
2009
Earlier work this paper cites.
Walters-Williams, J., Li, Y.: Estimation of mutual information: A survey. In: International Conference on Rough Sets and Knowledge Technology. pp. 389–396. Springer (2009). https://doi.org/10.1007/978-3-642-02962-2_49
2009
Earlier work this paper cites.
Altmann, A., Toloşi, L., Sander, O., Lengauer, T.: Permutation importance: a corrected feature importance measure. Bioinformatics 26
2010
Earlier work this paper cites.
Arlot, S., Celisse, A.: A survey of cross-validation procedures for model selection. Statist. Surv. 4
2010
Earlier work this paper cites.
He, Z., Yu, W.: Stable Feature Selection for Biomarker Discovery, vol. 34 (4), pp. 215–225. Computational Biology and Chemistry (aug 2010). https://doi.org/10.1016/j.compbiolchem.2010.07.002
2010
Earlier work this paper cites.
Wu, J., Roy, J., Stewart, W.F.: Prediction modeling using ehr data: challenges, strategies, and a comparison of machine learning approaches. Medical Care pp. S106–S113 (2010). https://doi.org/10.1097/MLR.0b013e3181de9e17
2010
Earlier work this paper cites.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12
2011
Earlier work this paper cites.
Reshef, D.N., Reshef, Y.A., Finucane, H.K., Grossman, S.R., McVean, G., Turnbaugh, P.J., Lander, E.S., Mitzenmacher, M., Sabeti, P.C.: Detecting novel associations in large data sets. Science 334
2011
Earlier work this paper cites.
Tibshirani, R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological) 58
2011
Earlier work this paper cites.
Bischl, B., Mersmann, O., Trautmann, H., Weihs, C.: Resampling methods for meta-model validation with recommendations for evolutionary computation. Evolutionary Computation 20
2012
Cited alongside, same era.
Cover, T.M., Thomas, J.A.: Elements of Information Theory. John Wiley & Sons (2012). https://doi.org/10.1002/047174882X
2012
Cited alongside, same era.
Good, P.I., Hardin, J.W.: Common errors in statistics (and how to avoid them). John Wiley & Sons (2012). https://doi.org/10.1002/9781118360125
2012
Cited alongside, same era.
Lopez-Paz, D., Hennig, P., Schölkopf, B.: The randomized dependence coefficient. In: Advances in Neural Information Processing Systems. pp. 1–9 (2013). https://doi.org/10.5555/2999611.2999612
2013
Cited alongside, same era.
Dickhaus, T.: Simultaneous Statistical Inference. Springer-Verlag Berlin Heidelberg (2014). https://doi.org/10.1007/978-3-642-45182-9
2019
Later among the works it cites.
Krishnan, M.: Against interpretability: a critical examination of the interpretability problem in machine learning. Philosophy & Technology (08 2019). https://doi.org/10.1007/s13347-019-00372-9
2019
Later among the works it cites.
König, G., Grosse-Wentrup, M.: A Causal Perspective on Challenges for AI in Precision Medicine (2019)
2019
Later among the works it cites.
Lang, M., Binder, M., Richter, J., Schratz, P., Pfisterer, F., Coors, S., Au, Q., Casalicchio, G., Kotthoff, L., Bischl, B.: mlr3: A modern object-oriented machine learning framework in R. Journal of Open Source Software (dec 2019). https://doi.org/10.21105/joss.01903
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2014
Cited alongside, same era.
Fernández-Delgado, M., Cernadas, E., Barro, S., Amorim, D.: Do we need hundreds of classifiers to solve real world classification problems. Journal of Machine Learning Research 15
2014
Cited alongside, same era.
Goldstein, A., Kapelner, A., Bleich, J., Pitkin, E.: Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation. Journal of Computational and Graphical Statistics 24
2014
Cited alongside, same era.
Shalev-Shwartz, S., Ben-David, S.: Understanding machine learning: From theory to algorithms. Cambridge university press (2014)
2014
Cited alongside, same era.
Štrumbelj, E., Kononenko, I.: Explaining prediction models and individual predictions with feature contributions. Knowledge and information systems 41
2014
Cited alongside, same era.
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., Elhadad, N.: Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. pp. 1721–1730 (2015). https://doi.org/10.1145/2783258.2788613
2015
Cited alongside, same era.
Gregorutti, B., Michel, B., Saint-Pierre, P.: Grouped variable importance with random forests and application to multiple functional data analysis. Computational Statistics & Data Analysis 90
2015
Cited alongside, same era.
Lessmann, S., Baesens, B., Seow, H.V., Thomas, L.C.: Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research 247
2015
Cited alongside, same era.
Molnar, C., Casalicchio, G., Bischl, B.: Quantifying model complexity via functional decomposition for better post-hoc interpretability. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. pp. 193–204. Springer (2019). https://doi.org/10.1007/978-3-030-43823-4_17
2019
Later among the works it cites.
Oh, S.: Feature interaction in terms of prediction performance. Applied Sciences 9
2019
Later among the works it cites.
Pearl, J., Mackenzie, D.: The ladder of causation. The book of why: the new science of cause and effect. New York (NY): Basic Books pp. 23–52 (2018). https://doi.org/10.1080/14697688.2019.1655928
2019
Later among the works it cites.
Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence 1
2019
Later among the works it cites.
Stachl, C., Au, Q., Schoedel, R., Buschek, D., Völkel, S., Schuwerk, T., Oldemeier, M., Ullmann, T., Hussmann, H., Bischl, B., et al.: Behavioral patterns in smartphone usage predict big five personality traits. PsyArXiv (2019). https://doi.org/10.31234/osf.io/ks4vd
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
Zhao, Q., Hastie, T.: Causal interpretations of black-box models. Journal of Business & Economic Statistics pp. 1–10 (2019). https://doi.org/10.1080/07350015.2019.1624293
2019
Later among the works it cites.
Apley, D.W., Zhu, J.: Visualizing the effects of predictor variables in black box supervised learning models. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 82
2020
Closest in time.
2020
Closest in time.
Covert, I., Lundberg, S.M., Lee, S.I.: Understanding global feature contributions with additive importance measures. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems. vol. 33, pp. 17212–17223. Curran Associates, Inc. (2020)
2020
Closest in time.
Dandl, S., Molnar, C., Binder, M., Bischl, B.: Multi-objective counterfactual explanations. In: International Conference on Parallel Problem Solving from Nature. pp. 448–469. Springer (2020). https://doi.org//10.1007/978-3-030-58112-1_31
2020
Closest in time.
2020
Closest in time.
Grömping, U.: Model-agnostic effects plots for interpreting machine learning models. Reports in Mathematics, Physics and Chemistry Report 1/2020
2020
Closest in time.
Karimi, A.H., Schölkopf, B., Valera, I.: Algorithmic Recourse: from Counterfactual Explanations to Interventions. arXiv: 2002.06278 (2020)
2020
Closest in time.
Lauritsen, S.M., Kristensen, M., Olsen, M.V., Larsen, M.S., Lauritsen, K.M., Jørgensen, M.J., Lange, J., Thiesson, B.: Explainable artificial intelligence model to predict acute critical illness from electronic health records. Nature communications 11
2020
Closest in time.
Lundberg, S.M., Erion, G., Chen, H., DeGrave, A., Prutkin, J.M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., Lee, S.I.: From local explanations to global understanding with explainable ai for trees. Nature machine intelligence 2
2020
Closest in time.
2020
Closest in time.
Moosbauer, J., Herbinger, J., Casalicchio, G., Lindauer, M., Bischl, B.: Towards explaining hyperparameter optimization via partial dependence plots. 8th ICML Workshop on Automated Machine Learning (AutoML) (2020)
2020
Closest in time.
Scholbeck, C.A., Molnar, C., Heumann, C., Bischl, B., Casalicchio, G.: Sampling, intervention, prediction, aggregation: A generalized framework for model-agnostic interpretations. Communications in Computer and Information Science p. 205–216 (2020). https://doi.org/10.1007/978-3-030-43823-4_18
2020
Closest in time.
Stachl, C., Au, Q., Schoedel, R., Gosling, S.D., Harari, G.M., Buschek, D., Theres, S., Völkel, Schuwerk, T., Oldemeier, M., Ullmann, T., Hussmann, H., Bischl, B., Bühner, M.: Predicting personality from patterns of behavior collected with smartphones. Proceedings of the National Academy of Sciences (2020). https://doi.org/10.1073/pnas.1920484117
2020
Closest in time.
Valentin, S., Harkotte, M., Popov, T.: Interpreting neural decoding models using grouped model reliance. PLOS Computational Biology 16
2020
Closest in time.
2020
Closest in time.
Zhao, X., Lovreglio, R., Nilsson, D.: Modelling and interpreting pre-evacuation decision-making using machine learning. Automation in Construction 113
2020
Closest in time.
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
König, G., Molnar, C., Bischl, B., Grosse-Wentrup, M.: Relative feature importance. In: 2020 25th International Conference on Pattern Recognition (ICPR). pp. 9318–9325. IEEE (2021). https://doi.org/10.1109/ICPR48806.2021.9413090
2021
Closest in time.
2021
Closest in time.
Seedorff, N., Brown, G.: totalvis: A principal components approach to visualizing total effects in black box models. SN Computer Science 2
2021
Closest in time.
Zhang, X., Wang, Y., Li, Z.: Interpreting the black box of supervised learning models: Visualizing the impacts of features on prediction. Applied Intelligence pp. 1–15 (2021). https://doi.org/10.1007/s10489-021-02255-z
2021
Closest in time.