Choosing the right regularization technique depends on the specific characteristics of the data and the research question. Here are some considerations:
Data Size and Dimensionality: For datasets with a large number of predictors relative to observations, Lasso or Elastic Net may be more appropriate. Correlation Among Predictors: If predictors are highly correlated, Elastic Net or PCA might be more suitable. Objective: If the goal is to identify a subset of important predictors, Lasso is often a good choice. For models where all predictors are expected to contribute, Ridge regression might be preferable. Prior Knowledge: If there is strong prior information available, Bayesian regularization can be advantageous.
