AWS Certified Machine Learning – Specialty — Question 127

A machine learning specialist works for a fruit processing company and needs to build a system that categorizes apples into three types. The specialist has collected a dataset that contains 150 images for each type of apple and applied transfer learning on a neural network that was pretrained on ImageNet with this dataset.
The company requires at least 85% accuracy to make use of the model.
After an exhaustive grid search, the optimal hyperparameters produced the following:
✑ 68% accuracy on the training set
✑ 67% accuracy on the validation set
What can the machine learning specialist do to improve the system's accuracy?

Answer options

• A. Upload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPO feature to optimize the model's hyperparameters.
• B. Add more data to the training set and retrain the model using transfer learning to reduce the bias.
• C. Use a neural network model with more layers that are pretrained on ImageNet and apply transfer learning to increase the variance.
• D. Train a new model using the current neural network architecture.

Correct answer: B

Explanation

The correct answer is B because increasing the training data helps the model learn better representations, which can reduce bias and improve accuracy. Options A and C focus on hyperparameter optimization and model architecture changes, respectively, which may not address the immediate need for more diverse training data. Option D suggests training a new model without addressing the dataset's limitations, which is not likely to yield better results.