{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T08:12:28Z","timestamp":1775808748792,"version":"3.50.1"},"reference-count":89,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,6,18]],"date-time":"2022-06-18T00:00:00Z","timestamp":1655510400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,6,18]],"date-time":"2022-06-18T00:00:00Z","timestamp":1655510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","award":["01IS18036A"],"award-info":[{"award-number":["01IS18036A"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Science Foundation","award":["SES-1758835"],"award-info":[{"award-number":["SES-1758835"]}]},{"DOI":"10.13039\/501100020639","name":"Bayerisches Staatsministerium f\u00fcr Wirtschaft, Landesentwicklung und Energie","doi-asserted-by":"crossref","award":["DIK-2106-0007 \/\/ DIK0260\/02"],"award-info":[{"award-number":["DIK-2106-0007 \/\/ DIK0260\/02"]}],"id":[{"id":"10.13039\/501100020639","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Interpretable machine learning has become a very active area of research due to the rising popularity of machine learning algorithms and their inherently challenging interpretability. Most work in this area has been focused on the interpretation of single features in a model. However, for researchers and practitioners, it is often equally important to quantify the importance or visualize the effect of feature groups. To address this research gap, we provide a comprehensive overview of how existing model-agnostic techniques can be defined for feature groups to assess the grouped feature importance, focusing on permutation-based, refitting, and Shapley-based methods. We also introduce an importance-based sequential procedure that identifies a stable and well-performing combination of features in the grouped feature space. Furthermore, we introduce the combined features effect plot, which is a technique to visualize the effect of a group of features based on a sparse, interpretable linear combination of features. We used simulation studies and real data examples to analyze, compare, and discuss these methods.<\/jats:p>","DOI":"10.1007\/s10618-022-00840-5","type":"journal-article","created":{"date-parts":[[2022,6,18]],"date-time":"2022-06-18T05:02:43Z","timestamp":1655528563000},"page":"1401-1450","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Grouped feature importance and combined features effect plot"],"prefix":"10.1007","volume":"36","author":[{"given":"Quay","family":"Au","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0430-8523","authenticated-orcid":false,"given":"Julia","family":"Herbinger","sequence":"additional","affiliation":[]},{"given":"Clemens","family":"Stachl","sequence":"additional","affiliation":[]},{"given":"Bernd","family":"Bischl","sequence":"additional","affiliation":[]},{"given":"Giuseppe","family":"Casalicchio","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,18]]},"reference":[{"key":"840_CR1","unstructured":"Allaire J, Gandrud C, Russell K, et\u00a0al (2017) networkD3: D3 JavaScript network graphs from R. https:\/\/CRAN.R-project.org\/package=networkD3, R package version 0.4"},{"issue":"12","key":"840_CR2","doi-asserted-by":"publisher","first-page":"6745","DOI":"10.1073\/pnas.96.12.6745","volume":"96","author":"U Alon","year":"1999","unstructured":"Alon U, Barkai N, Notterman DA et al (1999) Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci 96(12):6745\u20136750","journal-title":"Proc Natl Acad Sci"},{"key":"840_CR3","unstructured":"Amoukou SI, Brunel NJB, Sala\u00fcn T (2021) The shapley value of coalition of variables provides better explanations. arXiv:2103.13342"},{"key":"840_CR4","doi-asserted-by":"crossref","unstructured":"Apley DW, Zhu J (2019) Visualizing the effects of predictor variables in black box supervised learning models. arXiv:1612.08468","DOI":"10.1111\/rssb.12377"},{"issue":"473","key":"840_CR5","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1198\/016214505000000628","volume":"101","author":"E Bair","year":"2006","unstructured":"Bair E, Hastie T, Paul D et al (2006) Prediction by supervised principal components. J Am Stat Assoc 101(473):119\u2013137","journal-title":"J Am Stat Assoc"},{"issue":"7","key":"840_CR6","doi-asserted-by":"publisher","first-page":"1357","DOI":"10.1016\/j.patcog.2010.12.015","volume":"44","author":"E Barshan","year":"2011","unstructured":"Barshan E, Ghodsi A, Azimifar Z et al (2011) Supervised principal component analysis: visualization, classification and regression on subspaces and submanifolds. Pattern Recogn 44(7):1357\u20131371","journal-title":"Pattern Recogn"},{"issue":"1","key":"840_CR7","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1111\/j.1467-985X.2008.00556.x","volume":"172","author":"R Berk","year":"2009","unstructured":"Berk R, Sherman L, Barnes G et al (2009) Forecasting murder within a population of probationers and parolees: a high stakes application of statistical learning. J R Stat Soc A Stat Soc 172(1):191\u2013211","journal-title":"J R Stat Soc A Stat Soc"},{"issue":"1","key":"840_CR8","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"key":"840_CR9","unstructured":"Brenning A (2021) Transforming feature space to interpret machine learning models. arXiv:2104.04295"},{"key":"840_CR10","unstructured":"Caputo B, Sim K, Furesj\u00f6 F, et\u00a0al (2002) Appearance-based object recognition using SVMS: Which kernel should I use. In: Proceedings of the NIPS workshop on statistical methods for computational experiments in visual processing and computer vision, Red Hook, NY, USA"},{"key":"840_CR11","doi-asserted-by":"crossref","unstructured":"Casalicchio G, Molnar C, Bischl B (2019) Visualizing the feature importance for black box models. Springer International Publishing. Machine Learning and Knowledge Discovery in Databases, pp 655\u2013670","DOI":"10.1007\/978-3-030-10925-7_40"},{"issue":"3","key":"840_CR12","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1109\/TNN.2007.910730","volume":"19","author":"D Chakraborty","year":"2008","unstructured":"Chakraborty D, Pal NR (2008) Selecting useful groups of features in a connectionist framework. IEEE Trans Neural Netw 19(3):381\u2013396","journal-title":"IEEE Trans Neural Netw"},{"key":"840_CR13","unstructured":"Cohen SB, Ruppin E, Dror G (2005) Feature selection based on the Shapley value. In: Kaelbling LP, Saffiotti A (eds) IJCAI-05, Proceedings of the nineteenth international joint conference on artificial intelligence, Edinburgh, Scotland, UK, July 30\u2013August 5, 2005. Professional Book Center, pp 665\u2013670"},{"key":"840_CR14","first-page":"17212","volume":"33","author":"I Covert","year":"2020","unstructured":"Covert I, Lundberg SM, Lee SI (2020) Understanding global feature contributions with additive importance measures. Adv Neural Inf Process Syst 33:17212\u201317223","journal-title":"Adv Neural Inf Process Syst"},{"key":"840_CR15","unstructured":"de\u00a0Mijolla D, Frye C, Kunesch M, et\u00a0al (2020) Human-interpretable model explainability on high-dimensional data. CoRR arXiv:2010.07384"},{"issue":"3","key":"840_CR16","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/BF02288367","volume":"1","author":"C Eckart","year":"1936","unstructured":"Eckart C, Young G (1936) The approximation of one matrix by another of lower rank. Psychometrika 1(3):211\u2013218","journal-title":"Psychometrika"},{"issue":"177","key":"840_CR17","first-page":"1","volume":"20","author":"A Fisher","year":"2019","unstructured":"Fisher A, Rudin C, Dominici F (2019) All models are wrong, but many are useful: learning a variable\u2019s importance by studying an entire class of prediction models simultaneously. J Mach Learn Res 20(177):1\u201381","journal-title":"J Mach Learn Res"},{"key":"840_CR18","doi-asserted-by":"crossref","unstructured":"Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat, 1189\u20131232","DOI":"10.1214\/aos\/1013203451"},{"key":"840_CR19","unstructured":"Friedman J, Hastie T, Tibshirani R (2010) A note on the group lasso and a sparse group lasso. arXiv:1001.0736"},{"key":"840_CR20","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.csda.2014.07.001","volume":"81","author":"K Fuchs","year":"2015","unstructured":"Fuchs K, Scheipl F, Greven S (2015) Penalized scalar-on-functions regression with interaction term. Comput Stat Data Anal 81:38\u201351","journal-title":"Comput Stat Data Anal"},{"key":"840_CR21","first-page":"73","volume":"5","author":"K Fukumizu","year":"2004","unstructured":"Fukumizu K, Bach FR, Jordan MI (2004) Dimensionality reduction for supervised learning with reproducing kernel Hilbert spaces. J Mach Learn Res 5:73\u201399","journal-title":"J Mach Learn Res"},{"key":"840_CR22","doi-asserted-by":"crossref","unstructured":"Goldberg LR (1990) An alternative \u201cdescription of personality\u201d: the big-five factor structure. J Person Soc Psychol 59:1216\u20131229","DOI":"10.1037\/0022-3514.59.6.1216"},{"key":"840_CR23","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1080\/10618600.2014.907095","volume":"24","author":"A Goldstein","year":"2013","unstructured":"Goldstein A, Kapelner A, Bleich J et al (2013) Peeking inside the black box: visualizing statistical learning with plots of individual conditional expectation. J Comput Gr Stat 24:44\u201365","journal-title":"J Comput Gr Stat"},{"key":"840_CR24","unstructured":"Gregorova M, Kalousis A, Marchand-Maillet S (2018) Structured nonlinear variable selection. In: Globerson A, Silva R (eds) Proceedings of the thirty-fourth conference on uncertainty in artificial intelligence, UAI 2018, Monterey, California, USA, August 6\u201310, 2018. AUAI Press, pp 23\u201332"},{"key":"840_CR25","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.csda.2015.04.002","volume":"90","author":"B Gregorutti","year":"2015","unstructured":"Gregorutti B, Michel B, Saint-Pierre P (2015) Grouped variable importance with random forests and application to multiple functional data analysis. Comput Stat Data Anal 90:15\u201335","journal-title":"Comput Stat Data Anal"},{"key":"840_CR26","doi-asserted-by":"crossref","unstructured":"Gretton A, Bousquet O, Smola A, et\u00a0al (2005) Measuring statistical dependence with Hilbert-Schmidt norms. In: International conference on algorithmic learning theory. Springer, pp 63\u201377","DOI":"10.1007\/11564089_7"},{"issue":"1\u20133","key":"840_CR27","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1023\/A:1012487302797","volume":"46","author":"I Guyon","year":"2002","unstructured":"Guyon I, Weston J, Barnhill S et al (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46(1\u20133):389\u2013422","journal-title":"Mach Learn"},{"issue":"5","key":"840_CR28","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1002\/per.2032","volume":"29","author":"GM Harari","year":"2015","unstructured":"Harari GM, Gosling SD, Wang R et al (2015) Capturing situational information with smartphones and mobile sensing methods. Eur J Pers 29(5):509\u2013511","journal-title":"Eur J Pers"},{"issue":"6","key":"840_CR29","doi-asserted-by":"publisher","first-page":"838","DOI":"10.1177\/1745691616650285","volume":"11","author":"GM Harari","year":"2016","unstructured":"Harari GM, Lane ND, Wang R et al (2016) Using smartphones to collect behavioral data in psychological science: opportunities, practical considerations, and challenges. Perspect Psychol Sci 11(6):838\u2013854","journal-title":"Perspect Psychol Sci"},{"key":"840_CR30","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.cobeha.2017.07.018","volume":"18","author":"GM Harari","year":"2017","unstructured":"Harari GM, M\u00fcller SR, Aung MS et al (2017) Smartphone sensing methods for studying behavior in everyday life. Curr Opin Behav Sci 18:83\u201390","journal-title":"Curr Opin Behav Sci"},{"key":"840_CR31","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1037\/pspp0000245","volume":"119","author":"GM Harari","year":"2019","unstructured":"Harari GM, M\u00fcller SR, Stachl C et al (2019) Sensing sociability: individual differences in young adults\u2019 conversation, calling, texting, and app use behaviors in daily life. J Person Soc Psychol 119:204","journal-title":"J Person Soc Psychol"},{"key":"840_CR32","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.compbiolchem.2010.07.002","volume":"34","author":"Z He","year":"2010","unstructured":"He Z, Yu W (2010) Stable feature selection for biomarker discovery. Comput Biol Chem 34:215\u2013225","journal-title":"Comput Biol Chem"},{"key":"840_CR33","unstructured":"Hein M, Bousquet O (2004) Kernels, Associated structures and generalizations, Max Planck Institute for Biological Cybernetics"},{"issue":"1","key":"840_CR34","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1038\/nm0102-68","volume":"8","author":"MA Shipp","year":"2002","unstructured":"Shipp MA, Ross KN, Tamayo P et al (2002) Diffuse large b-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med 8(1):68\u201374","journal-title":"Nat Med"},{"key":"840_CR35","doi-asserted-by":"crossref","unstructured":"Hooker G (2004) Discovering additive structure in black box functions. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 575\u2013580","DOI":"10.1145\/1014052.1014122"},{"issue":"3","key":"840_CR36","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1198\/106186007X237892","volume":"16","author":"G Hooker","year":"2007","unstructured":"Hooker G (2007) Generalized functional anova diagnostics for high-dimensional functions of dependent variables. J Comput Graph Stat 16(3):709\u2013732","journal-title":"J Comput Graph Stat"},{"key":"840_CR37","unstructured":"Hooker G, Mentch L (2019) Please stop permuting features: an explanation and alternatives. arXiv:1905.03151"},{"issue":"4","key":"840_CR38","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1016\/j.jrp.2010.06.005","volume":"44","author":"JJ Jackson","year":"2010","unstructured":"Jackson JJ, Wood D, Bogg T et al (2010) What do conscientious people do? Development and validation of the behavioral indicators of conscientiousness (bic). J Res Pers 44(4):501\u2013511","journal-title":"J Res Pers"},{"key":"840_CR39","first-page":"53","volume":"8","author":"J Jaeger","year":"2003","unstructured":"Jaeger J, Sengupta R, Ruzzo W (2003) Improved gene selection for classification of microarrays. Pac Symp Biocomput Pac Symp Biocomput 8:53\u201364","journal-title":"Pac Symp Biocomput Pac Symp Biocomput"},{"key":"840_CR40","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-1904-8","volume-title":"Principal component analysis","author":"IT Jolliffe","year":"1986","unstructured":"Jolliffe IT (1986) Principal component analysis. Springer, New York"},{"issue":"11","key":"840_CR41","doi-asserted-by":"publisher","first-page":"1250","DOI":"10.3390\/electronics10111250","volume":"10","author":"T Kolenik","year":"2021","unstructured":"Kolenik T, Gams M (2021) Intelligent cognitive assistants for attitude and behavior change support in mental health: state-of-the-art technical review. Electronics 10(11):1250","journal-title":"Electronics"},{"issue":"523","key":"840_CR42","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1080\/01621459.2017.1307116","volume":"113","author":"J Lei","year":"2018","unstructured":"Lei J, G\u2019Sell M, Rinaldo A et al (2018) Distribution-free predictive inference for regression. J Am Stat Assoc 113(523):1094\u20131111","journal-title":"J Am Stat Assoc"},{"issue":"3","key":"840_CR43","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1145\/3236386.3241340","volume":"16","author":"ZC Lipton","year":"2018","unstructured":"Lipton ZC (2018) The mythos of model interpretability: in machine learning, the concept of interpretability is both important and slippery. Queue 16(3):31\u201357","journal-title":"Queue"},{"issue":"12","key":"840_CR44","doi-asserted-by":"publisher","first-page":"i110","DOI":"10.1093\/bioinformatics\/btp199","volume":"25","author":"AC Lozano","year":"2009","unstructured":"Lozano AC, Abe N, Liu Y et al (2009) Grouped graphical granger modeling for gene expression regulatory networks discovery. Bioinformatics 25(12):i110\u2013i118","journal-title":"Bioinformatics"},{"key":"840_CR45","unstructured":"Lundberg SM, Erion GG, Lee S (2018) Consistent individualized feature attribution for tree ensembles. CoRR arXiv:1802.03888"},{"key":"840_CR46","unstructured":"Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. In: Proceedings of the 31st international conference on neural information processing systems. Curran Associates Inc., Red Hook, NY, USA, NIPS\u201917, pp 4768\u20134777"},{"issue":"1","key":"840_CR47","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1111\/j.1467-9868.2007.00627.x","volume":"70","author":"L Meier","year":"2008","unstructured":"Meier L, Van De Geer S, B\u00fchlmann P (2008) The group lasso for logistic regression. J R Stat Soc Ser B (Stat Methodol) 70(1):53\u201371","journal-title":"J R Stat Soc Ser B (Stat Methodol)"},{"issue":"4","key":"840_CR48","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1111\/j.1467-9868.2010.00740.x","volume":"72","author":"N Meinshausen","year":"2010","unstructured":"Meinshausen N, B\u00fchlmann P (2010) Stability selection. J R Stat Soc Ser B (Stat Methodol) 72(4):417\u2013473","journal-title":"J R Stat Soc Ser B (Stat Methodol)"},{"issue":"3","key":"840_CR49","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1177\/1745691612441215","volume":"7","author":"G Miller","year":"2012","unstructured":"Miller G (2012) The smartphone psychology manifesto. Perspect Psychol Sci 7(3):221\u2013237","journal-title":"Perspect Psychol Sci"},{"key":"840_CR50","doi-asserted-by":"crossref","unstructured":"Molnar C (2019) Interpretable machine learning. https:\/\/christophm.github.io\/interpretable-ml-book\/","DOI":"10.21105\/joss.00786"},{"key":"840_CR51","unstructured":"Molnar C, K\u00f6nig G, Bischl B, et\u00a0al (2020a) Model-agnostic feature importance and effects with dependent features\u2014a conditional subgroup approach. arXiv:2006.04628"},{"key":"840_CR52","unstructured":"Molnar C, K\u00f6nig G, Herbinger J, et\u00a0al (2020b) General pitfalls of model-agnostic interpretation methods for machine learning models. arXiv preprint arXiv:2007.04131"},{"key":"840_CR53","doi-asserted-by":"crossref","unstructured":"Nicodemus K, Malley J, Strobl C, et\u00a0al (2010) The behaviour of random forest permutation-based variable importance measures under predictor correlation. BMC Bioinform 11\u2013110","DOI":"10.1186\/1471-2105-11-110"},{"issue":"7","key":"840_CR54","doi-asserted-by":"publisher","first-page":"1691","DOI":"10.1038\/npp.2016.7","volume":"41","author":"JP Onnela","year":"2016","unstructured":"Onnela JP, Rauch SL (2016) Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health. Neuropsychopharmacology 41(7):1691\u20131696","journal-title":"Neuropsychopharmacology"},{"key":"840_CR55","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1146\/annurev.psych.57.102904.190127","volume":"57","author":"DJ Ozer","year":"2006","unstructured":"Ozer DJ, Benet-Mart\u00ednez V (2006) Personality and the prediction of consequential outcomes. Annu Rev Psychol 57:401\u2013421","journal-title":"Annu Rev Psychol"},{"issue":"2","key":"840_CR56","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1093\/biostatistics\/kxl002","volume":"8","author":"MY Park","year":"2006","unstructured":"Park MY, Hastie T, Tibshirani R (2006) Averaged gene expressions for regression. Biostatistics 8(2):212\u2013227","journal-title":"Biostatistics"},{"issue":"1","key":"840_CR57","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1111\/rssb.12235","volume":"80","author":"N Pfister","year":"2017","unstructured":"Pfister N, B\u00fchlmann P, Sch\u00f6lkopf B et al (2017) Kernel-based tests for joint independence. J R Stat Soc Ser B (Stat Methodol) 80(1):5\u201331","journal-title":"J R Stat Soc Ser B (Stat Methodol)"},{"key":"840_CR58","doi-asserted-by":"crossref","unstructured":"Rachuri KK, Musolesi M, Mascolo C, et\u00a0al (2010) Emotionsense: a mobile phones based adaptive platform for experimental social psychology research. In: UbiComp\u201910\u2014Proceedings of the 2010 ACM conference on ubiquitous computing","DOI":"10.1145\/1864349.1864393"},{"issue":"3","key":"840_CR59","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1177\/0049124108330005","volume":"37","author":"M Raento","year":"2009","unstructured":"Raento M, Oulasvirta A, Eagle N (2009) Smartphones: an emerging tool for social scientists. Sociol Methods Res 37(3):426\u2013454","journal-title":"Sociol Methods Res"},{"issue":"13","key":"840_CR60","doi-asserted-by":"publisher","first-page":"i375","DOI":"10.1093\/bioinformatics\/btn188","volume":"24","author":"F Rapaport","year":"2008","unstructured":"Rapaport F, Barillot E, Vert JP (2008) Classification of Arraycgh data using fused SVM. Bioinformatics 24(13):i375\u2013i382","journal-title":"Bioinformatics"},{"key":"840_CR61","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.2537","volume":"4","author":"S Saeb","year":"2016","unstructured":"Saeb S, Lattie EG, Schueller SM et al (2016) The relationship between mobile phone location sensor data and depressive symptom severity. PeerJ 4:e2537","journal-title":"PeerJ"},{"issue":"4","key":"840_CR62","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1027\/2151-2604\/a000342","volume":"226","author":"R Schoedel","year":"2018","unstructured":"Schoedel R, Au Q, V\u00f6lkel ST et al (2018) Digital footprints of sensation seeking. Zeitschrift f\u00fcr Psychologie 226(4):232\u2013245","journal-title":"Zeitschrift f\u00fcr Psychologie"},{"key":"840_CR63","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1002\/per.2258","volume":"34","author":"R Schoedel","year":"2020","unstructured":"Schoedel R, Pargent F, Au Q et al (2020) To challenge the morning lark and the night owl: using smartphone sensing data to investigate day-night behaviour patterns. Eur J Personal 34:733\u2013752","journal-title":"Eur J Personal"},{"key":"840_CR64","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/978-3-030-43823-4_18","volume-title":"Machine learning and knowledge discovery in databases","author":"CA Scholbeck","year":"2020","unstructured":"Scholbeck CA, Molnar C, Heumann C et al (2020) Sampling, intervention, prediction, aggregation: a generalized framework for model-agnostic interpretations. In: Cellier P, Driessens K (eds) Machine learning and knowledge discovery in databases. Springer, Cham, pp 205\u2013216"},{"key":"840_CR65","doi-asserted-by":"publisher","first-page":"4193","DOI":"10.1007\/s10803-019-04134-6","volume":"49","author":"T Schuwerk","year":"2019","unstructured":"Schuwerk T, Kaltefleiter LJ, Au JQ et al (2019) Enter the wild: autistic traits and their relationship to mentalizing and social interaction in everyday life. J Autism Dev Disorders 49:4193\u20134208","journal-title":"J Autism Dev Disorders"},{"issue":"3","key":"840_CR66","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-021-00560-5","volume":"2","author":"N Seedorff","year":"2021","unstructured":"Seedorff N, Brown G (2021) totalvis: a principal components approach to visualizing total effects in black box models. SN Comput Sci 2(3):1\u201312","journal-title":"SN Comput Sci"},{"key":"840_CR67","doi-asserted-by":"crossref","unstructured":"Servia-Rodr\u00edguez S, Rachuri KK, Mascolo C, et\u00a0al (2017) Mobile sensing at the service of mental well-being: A large-scale longitudinal study. In: 26th international world wide web conference, WWW 2017. International World Wide Web Conferences Steering Committee, pp 103\u2013112","DOI":"10.1145\/3038912.3052618"},{"issue":"28","key":"840_CR68","first-page":"307","volume":"2","author":"LS Shapley","year":"1953","unstructured":"Shapley LS (1953) A value for n-person games. Contrib Theory Games 2(28):307\u2013317","journal-title":"Contrib Theory Games"},{"key":"840_CR69","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.engappai.2017.07.004","volume":"65","author":"S Sharifzadeh","year":"2017","unstructured":"Sharifzadeh S, Ghodsi A, Clemmensen LH et al (2017) Sparse supervised principal component analysis (sspca) for dimension reduction and variable selection. Eng Appl Artif Intell 65:168\u2013177","journal-title":"Eng Appl Artif Intell"},{"key":"840_CR70","first-page":"1393","volume":"13","author":"L Song","year":"2012","unstructured":"Song L, Smola A, Gretton A et al (2012) Feature selection via dependence maximization. J Mach Learn Res 13:1393\u20131434","journal-title":"J Mach Learn Res"},{"key":"840_CR71","doi-asserted-by":"crossref","unstructured":"Song L, Smola A, Gretton A, et\u00a0al (2007) Supervised feature selection via dependence estimation. In: Proceedings of the 24th international conference on Machine learning, pp 823\u2013830","DOI":"10.1145\/1273496.1273600"},{"issue":"6","key":"840_CR72","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1002\/per.2113","volume":"31","author":"C Stachl","year":"2017","unstructured":"Stachl C, Hilbert S, Au JQ et al (2017) Personality traits predict smartphone usage. Eur J Pers 31(6):701\u2013722","journal-title":"Eur J Pers"},{"key":"840_CR73","doi-asserted-by":"crossref","unstructured":"Stachl C, Au Q, Schoedel R et al (2020a) Predicting personality from patterns of behavior collected with smartphones. Proc Natl Acad Sci 117:17680\u201317687","DOI":"10.1073\/pnas.1920484117"},{"key":"840_CR74","doi-asserted-by":"crossref","unstructured":"Stachl C, Pargent F, Hilbert S et al (2020b) Personality research and assessment in the era of machine learning. Eur J Personal 34:613\u2013631","DOI":"10.1002\/per.2257"},{"key":"840_CR75","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1186\/1471-2105-9-307","volume":"9","author":"C Strobl","year":"2008","unstructured":"Strobl C, Boulesteix AL, Kneib T et al (2008) Conditional variable importance for random forests. BMC Bioinform 9:307","journal-title":"BMC Bioinform"},{"issue":"12","key":"840_CR76","doi-asserted-by":"publisher","first-page":"2692","DOI":"10.3390\/ijerph15122692","volume":"15","author":"S Thom\u00e9e","year":"2018","unstructured":"Thom\u00e9e S (2018) Mobile phone use and mental health; A review of the research that takes a psychological perspective on exposure. Int J Environ Res Public Health 15(12):2692","journal-title":"Int J Environ Res Public Health"},{"issue":"1","key":"840_CR77","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R (1996) Regression shrinkage and selection via the lasso. J Roy Stat Soc Ser B (Methodol) 58(1):267\u2013288","journal-title":"J Roy Stat Soc Ser B (Methodol)"},{"issue":"14","key":"840_CR78","doi-asserted-by":"publisher","first-page":"1986","DOI":"10.1093\/bioinformatics\/btr300","volume":"27","author":"L Tolo\u015fi","year":"2011","unstructured":"Tolo\u015fi L, Lengauer T (2011) Classification with correlated features: unreliability of feature ranking and solutions. Bioinformatics 27(14):1986\u20131994","journal-title":"Bioinformatics"},{"key":"840_CR79","doi-asserted-by":"crossref","unstructured":"Tripathi S, Hemachandra N, Trivedi P (2020) Interpretable feature subset selection: a Shapley value based approach. In: Proceedings of 2020 IEEE international conference on big data, special session on explainable artificial intelligence in safety critical systems","DOI":"10.1109\/BigData50022.2020.9378102"},{"key":"840_CR80","doi-asserted-by":"crossref","unstructured":"Valentin S, Harkotte M, Popov T (2020) Interpreting neural decoding models using grouped model reliance. PLOS Comput Biol 16(1):e1007148","DOI":"10.1371\/journal.pcbi.1007148"},{"key":"840_CR81","doi-asserted-by":"crossref","unstructured":"Venables B, Ripley B (2002) Modern applied statistics with S","DOI":"10.1007\/978-0-387-21706-2"},{"key":"840_CR82","unstructured":"Watson DS, Wright MN (2019) Testing conditional independence in supervised learning algorithms. arXiv:1901.09917"},{"key":"840_CR83","unstructured":"Williamson BD, Gilbert PB, Simon NR, et\u00a0al (2020) A unified approach for inference on algorithm-agnostic variable importance. arXiv:2004.03683"},{"key":"840_CR84","unstructured":"Williamson B, Feng J (2020) Efficient nonparametric statistical inference on population feature importance using Shapley values. In: International conference on machine learning, PMLR, pp 10282\u201310291"},{"issue":"2","key":"840_CR85","first-page":"1","volume":"1","author":"D Witten","year":"2020","unstructured":"Witten D, Tibshirani R (2020) PMA: penalized multivariate analysis. R Package Vers 1(2):1","journal-title":"R Package Vers"},{"issue":"3","key":"840_CR86","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1093\/biostatistics\/kxp008","volume":"10","author":"DM Witten","year":"2009","unstructured":"Witten DM, Tibshirani R, Hastie T (2009) A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10(3):515\u2013534","journal-title":"Biostatistics"},{"key":"840_CR87","first-page":"17","volume-title":"Multivariate data analysis in chemistry","author":"S Wold","year":"1984","unstructured":"Wold S, Albano C, Dunn WJ et al (1984) Multivariate data analysis in chemistry. Springer, Dordrecht, pp 17\u201395"},{"issue":"6","key":"840_CR88","doi-asserted-by":"publisher","first-page":"1100","DOI":"10.1177\/1745691617693393","volume":"12","author":"T Yarkoni","year":"2017","unstructured":"Yarkoni T, Westfall J (2017) Choosing prediction over explanation in psychology: lessons from machine learning. Perspect Psychol Sci 12(6):1100\u20131122","journal-title":"Perspect Psychol Sci"},{"issue":"1","key":"840_CR89","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1111\/j.1467-9868.2005.00532.x","volume":"68","author":"M Yuan","year":"2006","unstructured":"Yuan M, Lin Y (2006) Model selection and estimation in regression with grouped variables. J R Stat Soc Ser B (Stat Methodol) 68(1):49\u201367","journal-title":"J R Stat Soc Ser B (Stat Methodol)"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-022-00840-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10618-022-00840-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-022-00840-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T04:50:14Z","timestamp":1727412614000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10618-022-00840-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,18]]},"references-count":89,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["840"],"URL":"https:\/\/doi.org\/10.1007\/s10618-022-00840-5","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,18]]},"assertion":[{"value":"23 April 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 April 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}]}}