Which technique can help prevent a model from overfitting?

Regularization is a powerful technique used to prevent overfitting in predictive models. Overfitting occurs when a model learns not only the underlying patterns in the training data but also the noise and fluctuations, leading to poor generalization to new, unseen data.

Regularization introduces a penalty for complex models by adding an additional term to the loss function that the model is trying to minimize. This term discourages the model from fitting the noise in the training data by either constraining the size of the coefficients (as in L1 or Lasso regularization) or shrinking them towards zero (as in L2 or Ridge regularization). By doing so, regularization encourages simpler models that are less sensitive to fluctuations in the training data, thus improving the model's ability to generalize to new data.

In contrast, randomization may introduce variability that can lead to increased learning from the dataset but does not specifically address overfitting. Clustering is primarily used for grouping data and does not directly relate to overfitting in predictive modeling. Data augmentation can create more training examples and may help in some contexts, but it doesn’t directly alter the model's complexity or penalize overfitting like regularization does. Therefore, regularization stands out as the most