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Overfitting in machine learning occurs when a model is excessively complex and starts to learn both the underlying pattern and the noise in the training data. This results in a model that performs well on training data but poorly on unseen data, as it fails to generalize.

How Overfitting Happens

Overfitting is often a result of a model being too complex or having too many parameters relative to the amount of training data. It can be identified by a significant difference in performance between the training and validation datasets. Techniques to prevent overfitting include simplifying the model, using regularization methods, and increasing the size of the training dataset.

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