Regularization is a crucial concept in machine learning that helps prevent overfitting, which occurs when a model learns the noise in the training data rather than the underlying patterns. This can lead to poor performance on unseen data. Regularization techniques introduce additional information or constraints to the model, which can improve its generalization capabilities.

There are several classical regularization techniques, including L1 and L2 weight regularization, early stopping, and dropout. L1 regularization adds a penalty equal to the absolute value of the coefficients, while L2 regularization adds a penalty equal to the square of the coefficients. Early stopping involves monitoring the model’s performance on a validation set and stopping training when performance begins to degrade, thus preventing overfitting. Dropout randomly sets a fraction of the neurons to zero during training, which helps to create a more robust model.

Understanding and applying regularization techniques is essential for building effective machine learning models that perform well on new, unseen data ¹²³.