Overfitting prevention refers to techniques used to reduce the likelihood that a model will fit the noise in the training data instead of the actual underlying patterns. This is crucial in signal processing, as it ensures that models generalize well to unseen data and perform reliably in practical applications. Strategies like regularization, cross-validation, and early stopping are common methods to mitigate overfitting and enhance model performance.
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