What types of regularization are there? Can you name any specific ones?

Regularization is a method to combat overfitting in machine learning models, which adds a penalty for model complexity to the loss function.

Main types of regularization:

• L1 Regularization (Lasso): adds the sum of absolute coefficient values to the loss function. This leads to model sparsity, i.e., some coefficients become zero, which helps in feature selection.

• L2 Regularization (Ridge): adds the sum of squares of coefficients. It does not zero out coefficients but reduces their magnitude, stabilizing the model.

• Elastic Net: combines L1 and L2 regularization, balancing between sparsity and stability.

• Dropout (in neural networks): randomly "turns off" neurons during training to prevent overfitting.

Example of L2 regularization in Python using sklearn:

from sklearn.linear_model import Ridge
model = Ridge(alpha=1.0)  # alpha is the regularization coefficient
model.fit(X_train, y_train)