2nd Oct 2023

Regularization is a technique that is used to prevent overfitting in predictive models. Overfitting occurs when a model learns to fit the training data too closely, capturing noise and making it perform poorly on new, unseen data. Regularization introduces a penalty term to the model’s loss function, discouraging it from learning overly complex patterns. Two common forms of regularization are L1 regularization (Lasso) and L2 regularization (Ridge), which add constraints to the model’s coefficients to reduce their magnitude. By doing so, regularization helps strike a balance between fitting the training data well and maintaining the model’s ability to generalize to new data, ultimately improving its performance on unseen examples.