Regularization

Regularization is like adding a stabilizer to a bike - it helps the model not to overfit the training data by reducing its complexity. Think of it as a way to prevent the model from becoming too specialized in the training examples, so it can generalize better to new, unseen data. This is achieved by adding a penalty term to the loss function, which discourages large weights.