ML//neural network//overfitting

Overfitting occurs when a model captures patterns specific to its training sample that do not generalize to the target distribution. Memorization can contribute, but memorization and generalization are not strict opposites in large models.

Overfitting occurs when a model captures patterns specific to its training sample that do not generalize to the target distribution. Memorization can contribute, but memorization and generalization are not strict opposites in large models.

A widening train–validation gap is evidence, not a direct meter: leakage, distribution mismatch, label noise, and an unrepresentative validation set can produce similar symptoms.

More representative data, regularization, augmentation, early stopping, and appropriate capacity can help. “Simpler” is useful only relative to the evidence available.

The real test is not performance on data the model has not seen; it is performance on data drawn from the conditions where the model will actually be used.