L1 and L2 regularization are two different methods used to help choose the important features in a model. But they can make building the model a bit tricky.

1. L1 Regularization (Lasso):

   • This method tries to keep only the most important features by adding a penalty based on their absolute values.
   • Because of this, some features can end up with a value of zero, meaning they aren't used at all.
   • However, it can sometimes be unpredictable, especially when some features are very similar. This means the model might randomly pick one feature instead of another similar one.

2. L2 Regularization (Ridge):

   • This method adds a penalty based on the square of the coefficients, which usually makes all features smaller in value but doesn’t leave any out.
   • This can make it harder to choose important features since it keeps all of them. This might make the model harder to understand.

To help with these difficulties, people often use a mix of both regularization methods called Elastic Net. This approach combines the best parts of L1 and L2, helping to select important features while keeping the model stable.