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.