An insurer trained an Amazon SageMaker built-in Object Detection model (TensorFlow) to find scratches and dents in car photos. The model shows much better performance on the training set than on the test set. Which action is most appropriate to improve the model's performance on the test (validation) data?

The model performing significantly better on the training set than on the test set indicates overfitting. Overfitting occurs when a model learns the training data too well, including noise, and fails to generalize to unseen data. L2 regularization (weight decay) is a technique used to prevent overfitting by adding a penalty to the loss function proportional to the square of the magnitude of the weights. Increasing the L2 hyperparameter forces the model to use smaller weights, making it simpler and less prone to memorizing the training data, thereby improving its generalization to the test set. Reducing the learning rate might slow down convergence but doesn't directly address overfitting. Increasing momentum primarily helps accelerate convergence in the right direction but doesn't inherently prevent overfitting. Reducing the dropout rate would likely worsen overfitting, as dropout is a regularization technique that randomly deactivates neurons during training.