AWS Certified Machine Learning – Specialty — Question 314
An insurance company is creating an application to automate car insurance claims. A machine learning (ML) specialist used an Amazon SageMaker Object Detection - TensorFlow built-in algorithm to train a model to detect scratches and dents in images of cars. After the model was trained, the ML specialist noticed that the model performed better on the training dataset than on the testing dataset.
Which approach should the ML specialist use to improve the performance of the model on the testing data?
Answer options
- A. Increase the value of the momentum hyperparameter.
- B. Reduce the value of the dropout_rate hyperparameter.
- C. Reduce the value of the learning_rate hyperparameter
- D. Increase the value of the L2 hyperparameter.
Correct answer: D
Explanation
The model is suffering from overfitting because it generalizes poorly to the testing dataset despite scoring high on the training dataset. Increasing the L2 regularization hyperparameter penalizes larger weights, which simplifies the model and helps reduce overfitting. Conversely, decreasing the dropout rate would worsen overfitting, while adjusting learning rate or momentum does not directly address generalization issues.