L1 and L2 regularization are helpful tools in supervised learning, but they have some drawbacks. Here’s what I found:

• L1 Regularization (Lasso):

  • This method makes some numbers in the model go to zero. This is good because it helps choose the most important features. However, it can be affected by outliers, which means it may not always give consistent results.

• L2 Regularization (Ridge):

  • This method helps when features are closely related by keeping the size of the numbers in check. But it doesn't remove any features, so all of them stay in the model, which isn’t always the best choice.

• Combining Factors: Regularization can sometimes make models too simple. This means they might miss out on complicated relationships between the data.

From what I've seen, it’s important to try different approaches to find what works best for each specific dataset!