What is the purpose of regularization in AI models?

Regularization plays a critical role in the training of AI models by addressing the issue of overfitting. Overfitting occurs when a model learns the noise and details in the training data to the extent that it negatively impacts its performance on new, unseen data. This happens particularly in complex models that have a high capacity to learn the intricacies of the training dataset.

Regularization methods introduce a penalty for more complex models, which effectively discourages them from fitting too closely to the training data. By adjusting the model's learning process to favor simpler solutions, regularization helps maintain a balance between fitting the training data appropriately and ensuring that the model generalizes well to new data. This is typically accomplished through techniques such as L1 or L2 regularization, which add a term to the loss function that penalizes large coefficients in model parameters.

While regularization can indirectly impact model interpretability and feature selection, its primary and most significant purpose is to prevent overfitting. It does not inherently speed up training; rather, it can actually increase training complexity due to the additional computations involved.