Overfitting occurs when a machine learning model is overly complex or is trained too much on a given dataset, resulting in the model learning patterns in the training data that don't generalize well to unseen data. This can lead to the model performing poorly when it is used to make predictions on new data. An example of this could be a model trained on a dataset of images of cats that is so complex that it learns to recognize individual cats in the training set, but fails to recognize cats in general, resulting in poor performance when it is used to make predictions on a new dataset of images of cats.
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