When training machine learning models, we often encounter a common problem—overfitting. Overfitting happens when the model performs well on the training data but fails to generalize to unseen data.

Regularization is a method of adding a penalty term to the cost function of a linear regression model, which reduces the magnitude of the coefficients or weights. The penalty term can be either L1 or ...

L1 Regularization, also called Lasso Regularization, involves adding the absolute value of all weights to the loss value. L2 Regularization, also called Ridge Regularization, involves adding the ...