Which of the following is NOT a regularization technique?

Standardization is NOT a regularization technique; rather, it is a preprocessing step used to improve the performance of machine learning models. This process involves scaling the features of the dataset so that they have a mean of zero and a standard deviation of one. By standardizing the features, it helps to ensure that each feature contributes equally to the distance calculations, which can be particularly important for algorithms that rely on distance metrics, such as k-nearest neighbors or gradient descent optimization.

In contrast, Ridge, Lasso, and Elastic Net are all regularization techniques designed to prevent overfitting by adding a penalty term to the loss function during the training process. Ridge regression adds a penalty based on the square of the magnitude of the coefficients, which discourages the model from learning overly complex patterns. Lasso regression introduces a penalty based on the absolute values of the coefficients, which can lead to sparse models by driving some coefficients to zero. Elastic Net combines the penalties of both Ridge and Lasso, providing flexibility to balance between both techniques.

In summary, while Ridge, Lasso, and Elastic Net aim to enhance model generalization and performance by controlling complexity, standardization serves a different purpose in preparing data for analysis.