Learn in the textbookNeural Networks and Deep Learning
DefinitionOverfitting (過学習) is a state in which a model fits the training data used for learning so closely that its predictions on unseen data become less accurate.
ExplanationWhen a model memorizes even the chance quirks and noise in the training data, its grasp of general rules that hold for new data (generalization) weakens. You can spot it when accuracy is high on the training data but drops on data that was not used for learning. Ways to curb it include adding more training data and making the model simpler.
AnalogyLike a suit tailored so tightly to one person that it fits them perfectly but fits no one else at all.
Memory hookThe Japanese term 過学習 literally means "excess learning," and the English prefix "over-" says the same thing: the failure comes from going too far, not from learning too little.