A web company trains a deep learning multi-class model and sees 90% accuracy on training data but only 70% on test data (overfitting). To maximize accuracy on validation and test sets before deployment, which action is most likely to help the model generalize best?

The model is overfitting, indicated by high training accuracy (90%) and significantly lower test accuracy (70%). Overfitting means the model has learned the training data too well, including noise, and fails to generalize to unseen data. L1 and L2 regularization penalize large weights, discouraging complex models that fit noise. Dropout randomly deactivates neurons during training, preventing co-adaptation and forcing the network to learn more robust features. Both techniques are standard and effective ways to combat overfitting and improve generalization. Increasing randomness in mini-batches primarily helps with convergence and escaping local minima, but doesn't directly address overfitting. Allocating more data to training would exacerbate overfitting by giving the model even more data to memorize, potentially reducing the test set size further. Reducing network size can help with overfitting, but it might also lead to underfitting if the model becomes too simple to capture the underlying patterns; regularization and dropout are generally preferred as they allow the model to retain its capacity while preventing it from over-specializing.