Overfitting occurs when a machine learning model performs exceptionally well on training data but fails to generalize to new, unseen data. This happens because the model learns not only the underlying patterns in the training data, but also noise and random fluctuations. As a result, the model may have high accuracy on the training set, but perform poorly on the test set or real-world data.

For example, imagine a student who memorizes the answers to practice exam questions but doesn't understand the underlying concepts. They may do well on the practice exam, but struggle with a different set of questions. Similarly, an overfit model /“memorizes/” the training data rather than learning the true models.

To avoid overfitting:

Use cross-validation techniques to ensure that model performance is consistent across different subsets of data.
Simplify the model by reducing the number of parameters or features.
Use regularization methods to penalize overly complex models.
Gather more training data to provide a more comprehensive set of examples for the model to learn from.

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