In machine learning, which practice is employed to prevent a model from fitting too closely to training data?

Regularization is a crucial technique in machine learning that helps prevent a model from fitting too closely to the training data, often referred to as overfitting. This practice adds a penalty to the loss function used during model training, which effectively discourages the model from assigning too much importance to any individual feature. By doing so, regularization promotes the generalization capability of the model, allowing it to perform better on unseen data rather than merely memorizing the training set.

The implementation of regularization techniques, such as L1 (lasso) or L2 (ridge) regularization, enriches model training by balancing the trade-off between fitting the training data and maintaining a simplified model. This balance reduces the likelihood of overfitting and enhances the predictive performance on new, unseen datasets, which is a fundamental objective in developing effective machine learning models.