Overfitting
Overfitting is a common problem in machine learning where a model is trained too well on the training data and starts to memorize it instead of learning the underlying patterns. This results in a model that performs well on the training data but poorly on new, unseen data. Overfitting occurs when a model is too complex relative to the amount and noisiness of the training data. This can be mitigated by using techniques such as regularization, early stopping, and cross-validation. Regularization adds a penalty term to the loss function to discourage the model from fitting the noise in the data. Early stopping stops the training process when the model starts to overfit on the validation data. Cross-validation involves splitting the data into multiple folds and training the model on different combinations of folds to get a more accurate estimate of the model's performance.
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