Which approach should the ML specialist use to improve the performance of the model on the testing data?

Explanations:

Increasing the momentum hyperparameter typically helps with convergence speed during training, but it doesn’t directly address overfitting, which is likely the issue here.

Reducing the dropout_rate hyperparameter would lower regularization, making the model more likely to overfit, which could worsen performance on the testing dataset.

Reducing the learning rate could help with model convergence, but it won’t necessarily help with overfitting. A lower learning rate could make the model train more slowly, but won’t solve the core issue.

Increasing the L2 regularization hyperparameter (also known as weight decay) penalizes large weights, helping to prevent overfitting and improving generalization to the testing data.