When should I use regularization in my models?

When should I use regularization in my models?

Regularization is useful when you have a complex model that might overfit the training data. It helps to generalize better.

What are the common types of regularization techniques?

The most common types are L1 regularization (Lasso) and L2 regularization (Ridge). They add a penalty to the loss function.

How do I know if my model is overfitting?

You can check for overfitting by comparing the training and validation loss. If the training loss is much lower, your model may be overfitting.

Can regularization improve my model's performance?

Yes, regularization can improve performance by reducing overfitting, especially in models with many features.

Are there any downsides to using regularization?

One downside is that it can lead to underfitting if the regularization strength is too high, making the model too simple.