Which of the following techniques helps prevent overfitting in models?

Preventing overfitting is crucial for building robust machine learning models that generalize well to unseen data. Each of the techniques mentioned plays a significant role in achieving this goal.

Batch Normalization helps stabilize the learning process and can lead to faster convergence. By normalizing the inputs to each layer, it reduces the risk of internal covariate shift, which can lead to more stable gradients during training. While it primarily addresses training efficiency and helps in achieving better performance, it indirectly contributes to mitigating overfitting by allowing higher learning rates and improving the overall model's robustness.

Data augmentation involves artificially increasing the training dataset size by creating modified versions of the original data. This can include transformations like rotation, flipping, scaling, and cropping. By introducing variability in the training data, data augmentation helps the model to learn more generalized features, thus reducing the likelihood of overfitting to the specific details in the training set.

Dropout is a regularization technique that randomly sets a portion of the neurons to zero during training. This prevents the model from becoming too reliant on any specific set of features, encouraging it to develop a more distributed representation of the data. As a result, dropouts lead to a more robust model that can generalize better to new data.