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MAOverfitting occurs when a machine learning model learns the training data too well — capturing not just the underlying patterns but also the noise and random fluctuations specific to that dataset. The result is a model that performs excellently on training data but fails to generalize to new, unseen data. Think of it like a student who memorizes every answer from past exams verbatim: they’ll ace a repeated test but struggle the moment a question is phrased differently. In technical terms, the model has low bias but high variance — it’s overly sensitive to small changes in input.
The usual remedies for overfitting include regularization (like L1/L2 penalties that discourage large weights), dropout in neural networks, cross-validation to detect the gap between training and validation performance, early stopping during training, and simply using more training data when possible. Feature selection and pruning (in decision trees) also help by reducing model complexity. The core idea across all these techniques is the same: constrain the model so it captures the signal, not the noise.
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