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On Dropout, Overfitting, and Interaction Effects in Deep Neural Networks., , и . CoRR, (2020)Data Science with LLMs and Interpretable Models., , , и . CoRR, (2024)Bridging the Lexical Chasm: Statistical Approaches to Answer-Finding, , , , , и . In Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, (2000)The automatic training of rule bases that use numerical uncertainty representations.. Int. J. Approx. Reason., 2 (3): 330-331 (1988)Obtaining Calibrated Probabilities from Boosting, и . CoRR, (2012)Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data., , , , , и . PKDD/ECML Workshops (1), том 1524 из Communications in Computer and Information Science, стр. 534-551. Springer, (2021)Benefitting from the Variables that Variable Selection Discards., и . J. Mach. Learn. Res., (2003)Introduction to the Special Issue ACM SIGKDD 2012., , , и . ACM Trans. Knowl. Discov. Data, 7 (3): 9:1-9:2 (2013)Active Learning with Model Selection., , и . AAAI, стр. 1673-1679. AAAI Press, (2014)NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning., , и . ICLR, OpenReview.net, (2022)