AWS Certified Machine Learning – Specialty — Question 126

A web-based company wants to improve its conversion rate on its landing page. Using a large historical dataset of customer visits, the company has repeatedly trained a multi-class deep learning network algorithm on Amazon SageMaker. However, there is an overfitting problem: training data shows 90% accuracy in predictions, while test data shows 70% accuracy only.
The company needs to boost the generalization of its model before deploying it into production to maximize conversions of visits to purchases.
Which action is recommended to provide the HIGHEST accuracy model for the company's test and validation data?

Answer options

• A. Increase the randomization of training data in the mini-batches used in training
• B. Allocate a higher proportion of the overall data to the training dataset
• C. Apply L1 or L2 regularization and dropouts to the training
• D. Reduce the number of layers and units (or neurons) from the deep learning network

Correct answer: C

Explanation

Option C is correct because applying L1 or L2 regularization and incorporating dropouts are effective techniques to mitigate overfitting, helping the model generalize better on unseen data. Options A and B do not directly address overfitting and may worsen the problem, while option D could lead to underfitting by simplifying the model excessively.