What is regularization in machine learning?
An example of an AI chatbot conversation about regularization techniques in machine learning.
What is regularization in machine learning?
Regularization is a technique used to prevent overfitting by adding a penalty to the loss function.
Can you explain the different types of regularization?
Sure! The most common types are L1 (Lasso) and L2 (Ridge) regularization.
How does L1 regularization differ from L2?
L1 regularization can lead to sparse models by forcing some weights to be exactly zero, while L2 regularization shrinks weights but keeps all of them.
When should I use regularization?
You should use regularization when you have a complex model and want to avoid overfitting, especially with limited data.
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