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Double descent in statistics and machine learning is the phenomenon where a model with a small number of parameters and a model with an extremely large number of parameters have a small test error, but a model whose number of parameters is about the same as the number of data points used to train the model will have a large error.^[2] This phenomenon has been considered surprising, as it contradicts assumptions about overfitting in classical machine learning.^[3]

^ Rocks, Jason W. (2022). "Memorizing without overfitting: Bias, variance, and interpolation in overparameterized models". Physical Review Research. 4 (1). arXiv:2010.13933. doi:10.1103/PhysRevResearch.4.013201.
^ "Deep Double Descent". OpenAI. 2019-12-05. Retrieved 2022-08-12.
^ Schaeffer, Rylan; Khona, Mikail; Robertson, Zachary; Boopathy, Akhilan; Pistunova, Kateryna; Rocks, Jason W.; Fiete, Ila Rani; Koyejo, Oluwasanmi (2023-03-24). "Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle". arXiv:2303.14151v1 [cs.LG].

Double descent

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