Regularization

Regularization is a collective term for techniques that prevent overfitting in machine learning and deep learning by adding a penalty term to the loss function during training or by otherwise suppressing model complexity. Overfitting occurs when a model fits the training data too closely and performs poorly on unseen data—regularization is a key countermeasure. Common regularization techniques include: • L1 regularization (Lasso): Penalizes the sum of the absolute values of the weights, driving less important feature weights toward zero and promoting model sparsity. • L2 regularization (Ridge): Penalizes the sum of squared weights, suppressing large weights to improve model stability. • Dropout: Randomly deactivates a certain proportion of neurons during training, distributing the network's dependencies and improving generalization. • Early stopping: Halts training when the validation loss begins to worsen, preventing overfitting from progressing further. • Data augmentation: Artificially increases training data volume, enabling the model to handle more diverse inputs and providing an indirect regularization effect. Regularization is an essential technique for improving a model's generalization capability and building AI systems that perform well in practice. With the right combination of regularization methods, it is possible to achieve high accuracy while suppressing overfitting.