3-1-4. Regularization:
Regularization [13] is a method in machine learning aimed at reducing overfitting by adding a penalty term to the model’s loss function. Overfitting occurs when a model fits the training data too well, leading to poor performance on new data. The added penalty discourages the model from assigning too much weight to certain features and helps prevent overfitting. Different types of regularization exist, including L1, L2, early stopping, and dropout, and the choice of which to use depends on the problem and model. Regularization plays a crucial role in improving the performance of machine learning models.