Which technique is commonly used to prevent a model from overfitting?

Dropout is a widely used technique to prevent overfitting in neural networks. It works by randomly setting a fraction of the neurons to zero during training, which effectively removes them from the network at each training step. This process encourages the model to learn more robust features that are not overly reliant on any single neuron, thereby promoting generalization to new, unseen data. By preventing the network from becoming too complex and reliant on specific patterns in the training data, dropout helps to reduce the chance that the model will memorize the training dataset instead of learning to generalize from it.

Each of the other techniques mentioned, such as batch normalization, data augmentation, and regularization, serves useful roles in training models but approaches the problem of overfitting in different ways. For instance, batch normalization stabilizes and accelerates training but does not directly address overfitting like dropout does. Data augmentation generates more training examples through various transformations, which can help improve generalization but relies on the quality and diversity of the transformations applied. Regularization methods, such as L1 or L2 regularization, add penalties to the loss function to discourage complex models, which also addresses overfitting but in a different manner than dropout.