Why do we use Regularisation in ML models?

src: Analytics Vidhya

Regularization is a technique used in machine learning to prevent overfitting of a model. Overfitting occurs when a model is too complex, and it captures the noise in the data instead of the underlying pattern. This can lead to poor performance on new, unseen data.

Regularization helps to overcome overfitting by introducing a penalty term to the loss function, which discourages the model