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Question 292 - MLS-C01 discussion

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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?

A.

Increase the value of the momentum hyperparameter.

Answers
A.

Increase the value of the momentum hyperparameter.

B.

Reduce the value of the dropout_rate hyperparameter.

Answers
B.

Reduce the value of the dropout_rate hyperparameter.

C.

Reduce the value of the learning_rate hyperparameter.

Answers
C.

Reduce the value of the learning_rate hyperparameter.

D.

Increase the value of the L2 hyperparameter.

Answers
D.

Increase the value of the L2 hyperparameter.

Suggested answer: D

Explanation:

The machine learning model in this scenario shows signs of overfitting, as evidenced by better performance on the training dataset than on the testing dataset. Overfitting indicates that the model is capturing noise or details specific to the training data rather than general patterns.

One common approach to reduce overfitting is L2 regularization, which adds a penalty to the loss function for large weights and helps the model generalize better by smoothing out the weight distribution. By increasing the value of the L2 hyperparameter, the ML specialist can increase this penalty, helping to mitigate overfitting and improve performance on the testing dataset.

Options like increasing momentum or reducing dropout are less effective for addressing overfitting in this context.

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asked 31/10/2024
Mustafa Hussien
41 questions
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