Conditional variable importance for random forests

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538

The Elements of Statistical Learning

Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162

Bagging predictors

Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325

Unbiased Recursive Partitioning: A Conditional Inference Framework

Hothorn, T., Hornik, K., & Zeileis, A. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x133933 · Cited by 4,021

Permutation, Parametric and Bootstrap Tests of Hypotheses

(2005) · Springer Series in Statistics · doi:10.1007/b138696 · Cited by 707

Random Forests and Adaptive Nearest Neighbors

Lin, Y., & Jeon, Y. (2006) · Journal of the American Statistical Association · doi:10.1198/016214505000001230 · Cited by 507

A random forest guided tour

Biau, G., & Scornet, E. (2016) · Test · doi:10.1007/s11749-016-0481-7 · Cited by 4,149

Defining a Cancer Dependency Map

Tsherniak, A., Vazquez, F., Montgomery, P. G., Weir, B. A., Kryukov, G., Cowley, G. S., Gill, S., Harrington, W. F., Pantel, S., Krill-Burger, J. M., et al. (2017) · Cell · doi:10.1016/j.cell.2017.06.010 · Cited by 3,276

Variable selection using random forests

Genuer, R., Poggi, J.-M., & Tuleau-Malot, C. (2010) · Pattern Recognition Letters · doi:10.1016/j.patrec.2010.03.014 · Cited by 2,619

An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests.

Strobl, C., Malley, J., & Tutz, G. (2009) · Psychological Methods · doi:10.1037/a0016973 · Cited by 2,583

Definitions, methods, and applications in interpretable machine learning

Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900654116 · Cited by 2,167

All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously

Fisher, A., Rudin, C., & Dominici, F. (2018) · arXiv · doi:10.48550/arxiv.1801.01489 · Cited by 1,165

Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

Yu, K.-H., Zhang, C., Berry, G. J., Altman, R. B., Ré, C., Rubin, D. L., & Snyder, M. (2016) · Nature Communications · doi:10.1038/ncomms12474 · Cited by 972

Discovering the anticancer potential of non-oncology drugs by systematic viability profiling

Corsello, S. M., Nagari, R. T., Spangler, R. D., Rossen, J., Kocak, M., Bryan, J. G., Humeidi, R., Peck, D., Wu, X., Tang, A. A., et al. (2020) · Nature Cancer · doi:10.1038/s43018-019-0018-6 · Cited by 878

Predictive performance of presence‐only species distribution models: a benchmark study with reproducible code

Valavi, R., Guillera‐Arroita, G., Lahoz‐Monfort, J. J., & Elith, J. (2022) · Ecological Monographs · doi:10.1002/ecm.1486 · Cited by 830

Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges

Molnar, C., Casalicchio, G., & Bischl, B. (2020) · Communications in Computer and Information Science · doi:10.1007/978-3-030-65965-3_28 · Cited by 677

Opening the black box: the promise and limitations of explainable machine learning in cardiology

Petch, J., Di, S., & Nelson, W. (2022) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2021.09.004 · Cited by 552

Consistent Individualized Feature Attribution for Tree Ensembles

Lundberg, S. M., Erion, G. G., & Lee, S.-I. (2018) · arXiv · doi:10.48550/arxiv.1802.03888 · Cited by 1,430

A comparative study of machine learning classifiers for modeling travel mode choice

Hagenauer, J., & Helbich, M. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.01.057 · Cited by 424

Post-fire vegetation and fuel development influences fire severity patterns in reburns

Coppoletta, M., Merriam, K. E., & Collins, B. M. (2016) · Ecological Applications · doi:10.1890/15-0225 · Cited by 295

Auditing black-box models for indirect influence

Adler, P., Falk, C., Friedler, S. A., Nix, T., Rybeck, G., Scheidegger, C., Smith, B., & Venkatasubramanian, S. (2018) · Knowledge and Information Systems · doi:10.1007/s10115-017-1116-3 · Cited by 280

On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data

Schwarz, D. F., König, I. R., & Ziegler, A. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq257 · Cited by 269

Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data

Muchlinski, D., Siroky, D., He, J., & Kocher, M. (2016) · Political Analysis · doi:10.1093/pan/mpv024 · Cited by 234

Unrestricted permutation forces extrapolation: variable importance requires at least one more model, or there is no free variable importance

Hooker, G., Mentch, L., & Zhou, S. (2021) · Statistics and Computing · doi:10.1007/s11222-021-10057-z · Cited by 264

Interpretation of Quantitative Structure-Activity Relationship Models: Past, Present, and Future

Polishchuk, P. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00274 · Cited by 236

General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models

Molnar, C., König, G., Herbinger, J., Freiesleben, T., Dandl, S., Scholbeck, C. A., Casalicchio, G., Grosse-Wentrup, M., & Bischl, B. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-04083-2_4 · Cited by 211

White matter damage in frontotemporal lobar degeneration spectrum

Agosta, F., Scola, E., Canu, E., Marcone, A., Magnani, G., Sarro, L., Copetti, M., Caso, F., Cerami, C., Comi, G., et al. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhr288 · Cited by 180

Historical and current landscape-scale Ponderosa pine and mixed conifer forest structure in the Southern Sierra Nevada

Stephens, S. L., Lydersen, J. M., Collins, B. M., Fry, D. L., & Meyer, M. D. (2015) · Ecosphere · doi:10.1890/es14-00379.1 · Cited by 167

Sensitivity analysis methods in the biomedical sciences

Qian, G., & Mahdi, A. (2020) · Mathematical Biosciences · doi:10.1016/j.mbs.2020.108306 · Cited by 169

Severity of an uncharacteristically large wildfire, the Rim Fire, in forests with relatively restored frequent fire regimes

Lydersen, J. M., North, M. P., & Collins, B. M. (2014) · Forest Ecology and Management · doi:10.1016/j.foreco.2014.06.005 · Cited by 161

Clinical prediction from structural brain MRI scans: A large-scale empirical study

Initiative, F. T. A. D. N., Sabuncu, M. R., & Konukoglu, E. (2015) · Neuroinformatics · doi:10.1007/s12021-014-9238-1 · Cited by 161

An Empirical Study of Model-Agnostic Techniques for Defect Prediction Models

Jiarpakdee, J., Tantithamthavorn, C. K., Dam, H. K., & Grundy, J. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2020.2982385 · Cited by 148

Learning analytics to support self-regulated learning in asynchronous online courses: A case study at a women's university in South Korea

Kim, D., Yoon, M., Jo, I.-H., & Branch, R. M. (2018) · Computers & Education · doi:10.1016/j.compedu.2018.08.023 · Cited by 151

Forward Noise Adjustment Scheme for Data Augmentation

Moreno-Barea, F. J., Strazzera, F., Jerez, J. M., Urda, D., & Franco, L. (2018) · 2018 IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2018.8628917 · Cited by 158

Oxytocin efficacy is modulated by dosage and oxytocin receptor genotype in young adults with high-functioning autism: a 24-week randomized clinical trial

Kosaka, H., Okamoto, Y., Munesue, T., Yamasue, H., Inohara, K., Fujioka, T., Anme, T., Orisaka, M., Ishitobi, M., Jung, M., et al. (2016) · Translational Psychiatry · doi:10.1038/tp.2016.152 · Cited by 117

Interpretable Anomaly Detection with DIFFI: Depth-based feature importance of Isolation Forest

Carletti, M., Terzi, M., & Susto, G. A. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105730 · Cited by 118

A randomised clinical study to determine the effect of a toothpaste containing enzymes and proteins on plaque oral microbiome ecology

Adams, S. E., Arnold, D., Murphy, B., Carroll, P., Green, A. K., Smith, A. M., Marsh, P. D., Chen, T., Marriott, R. E., & Brading, M. G. (2017) · Scientific Reports · doi:10.1038/srep43344 · Cited by 111

Land-surface parameters for spatial predictive mapping and modeling

Maxwell, A. E., & Shobe, C. M. (2022) · Earth-Science Reviews · doi:10.1016/j.earscirev.2022.103944 · Cited by 105

A peek into the black box: exploring classifiers by randomization

Henelius, A., Puolamäki, K., Boström, H., Asker, L., & Papapetrou, P. (2014) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0368-8 · Cited by 98

Identification of important factors in an inpatient fall risk prediction model to improve the quality of care using EHR and electronic administrative data: A machine-learning approach

Lindberg, D. S., Prosperi, M., Bjarnadottir, R. I., Thomas, J., Crane, M., Chen, Z., Shear, K., Solberg, L. M., Snigurska, U. A., Wu, Y., et al. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104272 · Cited by 95

Tree-based ensemble methods for sensitivity analysis of environmental models: A performance comparison with Sobol and Morris techniques

Jaxa-Rozen, M., & Kwakkel, J. (2018) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2018.06.011 · Cited by 91

Conceptual challenges for interpretable machine learning

Watson, D. S. (2022) · Synthese · doi:10.1007/s11229-022-03485-5 · Cited by 87

Precision medicine for long-term depression outcomes using the Personalized Advantage Index approach: cognitive therapy or interpersonal psychotherapy?

van Bronswijk, S. C., DeRubeis, R. J., Lemmens, L. H. J. M., Peeters, F. P. M. L., Keefe, J. R., Cohen, Z. D., & Huibers, M. J. H. (2021) · Psychological Medicine · doi:10.1017/s0033291719003192 · Cited by 75

Metabolic Networks and Metabolites Underlie Associations Between Maternal Glucose During Pregnancy and Newborn Size at Birth.

Scholtens, D. M., Bain, J. R., Reisetter, A. C., Muehlbauer, M. J., Nodzenski, M., Stevens, R. D., Ilkayeva, O., Lowe, L. P., Metzger, B. E., Newgard, C. B., et al. (2016) · Diabetes · doi:10.2337/db15-1748 · Cited by 69

An Untargeted Metabolomics Approach to Characterize Short-Term and Long-Term Metabolic Changes after Bariatric Surgery

Narath, S. H., Mautner, S. I., Svehlikova, E., Schultes, B., Pieber, T. R., Sinner, F. M., Gander, E., Libiseller, G., Schimek, M. G., Sourij, H., et al. (2016) · PLOS ONE · doi:10.1371/journal.pone.0161425 · Cited by 65

Emergent behaviour and neural dynamics in artificial agents tracking odour plumes

Singh, S. H., van Breugel, F., Rao, R. P. N., & Brunton, B. W. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-022-00599-w · Cited by 61

Development of PancRISK, a urine biomarker-based risk score for stratified screening of pancreatic cancer patients

Blyuss, O., Zaikin, A., Cherepanova, V., Munblit, D., Kiseleva, E. M., Prytomanova, O. M., Duffy, S. W., & Crnogorac-Jurcevic, T. (2020) · British Journal of Cancer · doi:10.1038/s41416-019-0694-0 · Cited by 67

Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis

Creagh, A. P., Hamy, V., Yuan, H., Mertes, G., Tomlinson, R., Chen, W.-H., Williams, R., Llop, C., Yee, C., Duh, M. S., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01013-y · Cited by 64

African elephants address one another with individually specific name-like calls

Pardo, M. A., Fristrup, K., Lolchuragi, D. S., Poole, J. H., Granli, P., Moss, C., Douglas-Hamilton, I., & Wittemyer, G. (2024) · Nature Ecology & Evolution · doi:10.1038/s41559-024-02420-w · Cited by 62

Multiparametric MRI to distinguish early onset Alzheimer's disease and behavioural variant of frontotemporal dementia

Canu, E., Agosta, F., Mandic-Stojmenovic, G., Stojković, T., Stefanova, E., Inuggi, A., Imperiale, F., Copetti, M., Kostic, V. S., & Filippi, M. (2017) · NeuroImage: Clinical · doi:10.1016/j.nicl.2017.05.018 · Cited by 62

Twelve key challenges in medical machine learning and solutions

Ellis, R. J., Sander, R. M., & Limon, A. (2022) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2022.100068 · Cited by 61

Measuring the stability of results from supervised statistical learning

Philipp, M., Rusch, T., Hornik, K., & Strobl, C. (2018) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2018.1473779 · Cited by 56

Reducing Dropout in Treatment for Depression: Translating Dropout Predictors Into Individualized Treatment Recommendations

Zilcha-Mano, S., Keefe, J. R., Chui, H., Rubin, A., Barrett, M. S., & Barber, J. P. (2016) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.15m10081 · Cited by 55

Response of the human gut and saliva microbiome to urbanization in Cameroon

Lokmer, A., Aflalo, S., Amougou, N., Lafosse, S., Froment, A., Tabe, F. E., Poyet, M., Groussin, M., Said-Mohamed, R., & Ségurel, L. (2020) · Scientific Reports · doi:10.1038/s41598-020-59849-9 · Cited by 46

Lessons and tips for designing a machine learning study using EHR data

Arbet, J., Brokamp, C., Meinzen-Derr, J., Trinkley, K. E., & Spratt, H. M. (2021) · Journal of Clinical and Translational Science · doi:10.1017/cts.2020.513 · Cited by 46

Training set design for machine learning techniques applied to the approximation of computationally intensive first-principles kinetic models

Bracconi, M., & Maestri, M. (2020) · Chemical Engineering Journal · doi:10.1016/j.cej.2020.125469 · Cited by 48

From observation to information: data-driven understanding of on farm yield variation

Jiménez, D., Dorado, H., Cock, J., Prager, S. D., Delerce, S., Grillon, A., Andrade Bejarano, M., Benavides, H., & Jarvis, A. (2016) · PLOS ONE · doi:10.1371/journal.pone.0150015 · Cited by 47

A scalable scheme to implement data-driven agriculture for small-scale farmers

Jiménez, D., Delerce, S., Dorado, H., Cock, J., Muñoz, L. A., Agamez, A., & Jarvis, A. (2019) · Global Food Security · doi:10.1016/j.gfs.2019.08.004 · Cited by 47

AcME - accelerated model-agnostic explanations: fast whitening of the machine-learning black box

Dandolo, D., Masiero, C., Carletti, M., Dalle Pezze, D., & Susto, G. A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119115 · Cited by 41

Data consistency in the English Hospital Episodes Statistics database

Hardy, F., Heyl, J., Tucker, K., Hopper, A., Marchã, M. J., Briggs, T. W. R., Yates, J., Day, J., Wheeler, A., Eve-Jones, S., et al. (2022) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2022-100633 · Cited by 38

Spatial machine-learning model diagnostics: a model-agnostic distance-based approach

Brenning, A. (2023) · International Journal of Geographical Information Science · doi:10.1080/13658816.2022.2131789 · Cited by 33

A comparative study of patient and staff safety evaluation using tree-based machine learning algorithms

Simsekler, M. C. E., Rodrigues, C., Qazi, A., Ellahham, S., & Ozonoff, A. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2020.107416 · Cited by 30

Performance of early risk assessment tools to predict the later development of gestational diabetes

Kotzaeridi, G., Blätter, J., Eppel, D., Rosicky, I., Mittlböck, M., Yerlikaya‐Schatten, G., Schatten, C., Husslein, P., Eppel, W., Huhn, E. A., et al. (2021) · European Journal of Clinical Investigation · doi:10.1111/eci.13630 · Cited by 27

Interpreting machine-learning models in transformed feature space with an application to remote-sensing classification

Brenning, A. (2023) · Machine Learning · doi:10.1007/s10994-023-06327-8 · Cited by 26

Principles of mRNA control by human PUM proteins elucidated from multimodal experiments and integrative data analysis

Wolfe, M. B., Schagat, T. L., Paulsen, M. T., Magnuson, B., Ljungman, M., Park, D., Zhang, C., Campbell, Z. T., Goldstrohm, A. C., & Freddolino, P. L. (2020) · RNA · doi:10.1261/rna.077362.120 · Cited by 22

Spatial Simulation Modeling of Settlement Distribution Driven by Random Forest: Consideration of Landscape Visibility

Zheng, M., Tang, W., Ogundiran, A., & Yang, J. (2020) · Sustainability · doi:10.3390/su12114748 · Cited by 19

Moderators of response to child‐based and parent‐based child anxiety treatment: a machine learning‐based analysis

Lebowitz, E. R., Zilcha‐Mano, S., Orbach, M., Shimshoni, Y., & Silverman, W. K. (2021) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13386 · Cited by 19

Modeling sources of interlaboratory variability in electrophysiological properties of mammalian neurons

Tebaykin, D., Tripathy, S. J., Binnion, N., Li, B., Gerkin, R. C., & Pavlidis, P. (2018) · Journal of Neurophysiology · doi:10.1152/jn.00604.2017 · Cited by 15

Daily distracted consumption patterns and their relationship with BMI

van Meer, F., de Vos, F., Hermans, R. C. J., Peeters, P. A., & van Dillen, L. F. (2022) · Appetite · doi:10.1016/j.appet.2022.106136 · Cited by 14

Testing conditional independence in supervised learning algorithms

Watson, D. S., & Wright, M. N. (2021) · Machine Learning · doi:10.1007/s10994-021-06030-6 · Cited by 13

Importance of Non-invasive Right and Left Ventricular Variables on Exercise Capacity in Patients with Tetralogy of Fallot Hemodynamics

Meierhofer, C., Tavakkoli, T., Kühn, A., Ulm, K., Hager, A., Müller, J., Martinoff, S., Ewert, P., & Stern, H. (2017) · Pediatric Cardiology · doi:10.1007/s00246-017-1697-3 · Cited by 11

Comments on: A random forest guided tour

Hooker, G., & Mentch, L. (2016) · Test · doi:10.1007/s11749-016-0485-3 · Cited by 7