What are some examples of regularization techniques in machine learning?

What are some examples of regularization techniques in machine learning?

Regularization techniques help prevent overfitting in machine learning models. Common examples include L1 and L2 regularization, dropout, and early stopping.

Can you explain L1 and L2 regularization?

Sure! L1 regularization adds a penalty equal to the absolute value of the coefficients, promoting sparsity. L2 regularization adds a penalty equal to the square of the coefficients, which helps to keep the weights small.

What is dropout?

Dropout is a technique where randomly selected neurons are ignored during training, which helps to prevent overfitting by ensuring that the model does not rely too heavily on any individual neuron.

How does early stopping work?

Early stopping involves monitoring the model's performance on a validation set and stopping training when performance starts to degrade, preventing overfitting.