Overfitting: the model learned noise, not the pattern

A model with 50 features on 60 examples. On train it is perfect: passes through every point. On test—disaster. That is overfitting: the model memorized random noise in the training set instead of real structure. Weights blew up; each feature pulled the blanket its way. Regularization—a penalty for large weights—brings the model back to sanity.

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