Double descent

Machine learning and data mining
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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 both have a small training 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 much greater test error than one with a much larger number of parameters. This phenomenon has been considered surprising, as it contradicts assumptions about overfitting in classical machine learning.