Regularization in Machine Learning: Preventing Overfitting with L1, L2, and Dropout

This article explains the problem of overfitting in machine learning models and how regularization techniques like L1, L2, dropout, and early stopping can be used to control model complexity and improve generalization.

Details

Overfitting is a common issue in machine learning where a model performs well on the training data but fails to generalize to new, unseen data. Regularization is a technique used to control model complexity and prevent overfitting. The core idea is to add a penalty term Ω(w) to the training loss E_train(w), resulting in an augmented loss E_aug(w) = E_train(w) + λΩ(w), where λ is a hyperparameter that controls the strength of regularization. L2 regularization (also known as weight decay) smooths the weights and stabilizes training, while L1 regularization promotes sparse weights and enables feature selection. Early stopping halts training when the validation loss starts increasing, preventing the model from memorizing the training data. Dropout randomly disables neurons during training, reducing co-adaptation and improving generalization. The article recommends starting with L2 regularization, adding early stopping, and then considering dropout or L1 if needed.