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Question 143 - MLS-C01 discussion

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A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat. The ML Specialist performs some tests and records the following results for a neural network-based image classifier:

Total number of images available = 1,000 Test set images = 100 (constant test set)

The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.

Which techniques can be used by the ML Specialist to improve this specific test error?

A.
Increase the training data by adding variation in rotation for training images.
Answers
A.
Increase the training data by adding variation in rotation for training images.
B.
Increase the number of epochs for model training.
Answers
B.
Increase the number of epochs for model training.
C.
Increase the number of layers for the neural network.
Answers
C.
Increase the number of layers for the neural network.
D.
Increase the dropout rate for the second-to-last layer.
Answers
D.
Increase the dropout rate for the second-to-last layer.
Suggested answer: A

Explanation:

To improve the test error for the image classifier, the Machine Learning Specialist should use the technique of increasing the training data by adding variation in rotation for training images. This technique is called data augmentation, which is a way of artificially expanding the size and diversity of the training dataset by applying various transformations to the original images, such as rotation, flipping, cropping, scaling, etc. Data augmentation can help the model learn more robust features that are invariant to the orientation, position, and size of the objects in the images.This can improve the generalization ability of the model and reduce the test error, especially for cases where the images are not well-aligned or have different perspectives1.

References:

1: Image Augmentation - Amazon SageMaker

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asked 16/09/2024
David Hartnett
41 questions
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