A condition where a model learns patterns specific to its training data that don't generalize to unseen data, resulting in high training accuracy but poor re...
When an AI memorizes answers instead of truly learning — like acing a test by memorizing answers but not understanding the subject.
When a model learns the training data too well, including its noise and quirks, so it performs great on practice data but poorly on new data.
A condition where a model learns patterns specific to its training data that don't generalize to unseen data, resulting in high training accuracy but poor real-world performance.
Excessive model complexity relative to training data, where the model captures noise and idiosyncrasies rather than underlying patterns — mitigated by regularization, dropout, early stopping, and data augmentation.
A regime where empirical risk on the training set decreases while true risk increases — diagnosable via train-test divergence curves, addressable through capacity control (L1/L2 regularization, dropout, weight decay) and the double descent phenomenon in overparameterized models.
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