| Quantifying uncertainty in random forests via confidence intervals and hypothesis tests L Mentch, G Hooker The Journal of Machine Learning Research 17 (1), 841-881, 2016 | 359 | 2016 |
| Please stop permuting features: An explanation and alternatives G Hooker, L Mentch arXiv preprint arXiv:1905.03151 2, 2019 | 141 | 2019 |
| Unrestricted permutation forces extrapolation: variable importance requires at least one more model, or there is no free variable importance G Hooker, L Mentch, S Zhou Statistics and Computing 31, 1-16, 2021 | 130 | 2021 |
| Randomization as regularization: A degrees of freedom explanation for random forest success L Mentch, S Zhou Journal of Machine Learning Research 21 (171), 1-36, 2020 | 66 | 2020 |
| Integrative modelling of tumour DNA methylation quantifies the contribution of metabolism M Mehrmohamadi, LK Mentch, AG Clark, JW Locasale Nature communications 7 (1), 13666, 2016 | 49 | 2016 |
| Earlier Isn't Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization T Jung, D Kang, L Mentch, E Hovy arXiv preprint arXiv:1908.11723, 2019 | 47 | 2019 |
| Formal hypothesis tests for additive structure in random forests L Mentch, G Hooker Journal of Computational and Graphical Statistics 26 (3), 589-597, 2017 | 44* | 2017 |
| R-CMap—An open-source software for concept mapping H Bar, L Mentch Evaluation and Program Planning 60, 284-292, 2017 | 43 | 2017 |
| The importance of calibration in clinical psychology O Lindhiem, IT Petersen, LK Mentch, EA Youngstrom Assessment 27 (4), 840-854, 2020 | 39 | 2020 |
| Rates of convergence for random forests via generalized U-statistics W Peng, T Coleman, L Mentch Electronic Journal of Statistics 16 (1), 232-292, 2022 | 30 | 2022 |
| Scalable and efficient hypothesis testing with random forests T Coleman, W Peng, L Mentch Journal of Machine Learning Research 23 (170), 1-35, 2022 | 28 | 2022 |
| Predictive inference with random forests: A new perspective on classical analyses RJ McAlexander, L Mentch Research & Politics 7 (1), 2053168020905487, 2020 | 28 | 2020 |
| Asymptotic distributions and rates of convergence for random forests and other resampled ensemble learners W Peng, T Coleman, L Mentch arXiv preprint arXiv:1905.10651, 2019 | 18 | 2019 |
| Bootstrap bias corrections for ensemble methods G Hooker, L Mentch Statistics and Computing 28, 77-86, 2018 | 18 | 2018 |
| Trees, forests, chickens, and eggs: when and why to prune trees in a random forest S Zhou, L Mentch Statistical Analysis and Data Mining: The ASA Data Science Journal 16 (1), 45-64, 2023 | 17 | 2023 |
| Asymptotic distributions and rates of convergence for random forests via generalized U-statistics W Peng, T Coleman, L Mentch arXiv preprint arXiv:1905.10651, 2019 | 17 | 2019 |
| Physiological sleep measures predict time to 15‐year mortality in community adults: application of a novel machine learning framework ML Wallace, TS Coleman, LK Mentch, DJ Buysse, JL Graves, EW Hagen, ... Journal of sleep research 30 (6), e13386, 2021 | 14 | 2021 |
| V-statistics and variance estimation Z Zhou, L Mentch, G Hooker Journal of Machine Learning Research 22 (287), 1-48, 2021 | 14 | 2021 |
| Smudge noise for quality estimation of fingerprints and its validation R Richter, C Gottschlich, L Mentch, DH Thai, SF Huckemann IEEE Transactions on Information Forensics and Security 14 (8), 1963-1974, 2019 | 14 | 2019 |
| Forward stability and model path selection N Kissel, L Mentch Statistics and Computing 34 (2), 82, 2024 | 13 | 2024 |
Giles HookerProfessor of Statistics, University of PennsylvaniaVerified email at wharton.upenn.edu
