CT-AI · Question #27
A wildlife conservation group would like to use a neural network to classify images of different animals. The algorithm is going to be used on a social media platform to automatically pick out…
The correct answer is D. Back-to-back testing using the version of the model before training and the new version of the. The syllabus defines back-to-back testing as a method to compare a modified AI system to the previous version, which is ideal in this scenario: "Back-to-back testing is performed by comparing the outputs of two systems that are supposed to provide the same outputs, one being a…
Question
A wildlife conservation group would like to use a neural network to classify images of different animals. The algorithm is going to be used on a social media platform to automatically pick out pictures of the chosen animal of the month. This month's animal is set to be a wolf. The test team has already observed that the algorithm could classify a picture of a dog as being a wolf because of the similar characteristics between dogs and wolves. To handle such instances, the team is planning to train the model with additional images of wolves and dogs so that the model is able to better differentiate between the two. What test method should you use to verify that the model has improved after the additional training?
Options
- AMetamorphic testing because the application domain is not clearly understood at this point.
- BAdversarial testing to verify that no incorrect images have been used in the training.
- CPairwise testing using combinatorics to look at a long list of photo parameters.
- DBack-to-back testing using the version of the model before training and the new version of the
How the community answered
(35 responses)- A3% (1)
- B9% (3)
- C3% (1)
- D86% (30)
Explanation
The syllabus defines back-to-back testing as a method to compare a modified AI system to the previous version, which is ideal in this scenario: "Back-to-back testing is performed by comparing the outputs of two systems that are supposed to provide the same outputs, one being a known and trusted system and the other being the test system. This approach can be used to test ML systems after re-training to verify that improvements have not introduced regressions."
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