👉 Regularization is a technique used in machine learning and statistics to prevent overfitting or underfitting models. It involves adding a penalty term to the loss function that penalizes the model's performance for its inability to learn complex patterns or relationships in the data. The goal of regularization is to make the model more robust to noise, making it less prone to overfitting and better generalization.
