Which method is typically used for feature transformation in machine learning?

Normalization is indeed a common method used for feature transformation in machine learning. It involves adjusting the values in a dataset to a common scale without distorting differences in the ranges of values. This is particularly important when features have different units or scales, as many machine learning algorithms perform better when features are on a similar scale. For example, normalization can help algorithms like k-nearest neighbors or gradient descent converge faster and improve performance.

In contrast, encoding refers to the process of converting categorical variables into numerical formats that can be used in machine learning models, which is more about representation rather than transformation. Regularization is a technique used to prevent overfitting by adding a penalty to the loss function during model training, which does not directly relate to feature transformation. Clustering refers to grouping similar data points together based on certain characteristics and is not a feature transformation method but rather a data analysis technique. Therefore, normalization stands out as the appropriate method for adjusting feature values to enhance model performance.