Topic 1 Question 153
A manufacturing company asks its machine learning specialist to develop a model that classifies defective parts into one of eight defect types. The company has provided roughly 100,000 images per defect type for training. During the initial training of the image classification model, the specialist notices that the validation accuracy is 80%, while the training accuracy is 90%. It is known that human-level performance for this type of image classification is around 90%. What should the specialist consider to fix this issue?
A longer training time
Making the network larger
Using a different optimizer
Using some form of regularization
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コメント(6)
D - over fitting problem.
👍 16bluer12022/05/01- 正解だと思う選択肢: D
The specialist should consider using some form of regularization to fix this issue. Regularization techniques such as dropout or L2 regularization can help prevent overfitting, which can occur when the model performs well on the training data but poorly on the validation data.
Option A, a longer training time, might not necessarily fix the issue and could lead to overfitting if the model is already performing well on the training data.
Option B, making the network larger, could also lead to overfitting and may not be necessary if the current network architecture is sufficient to perform the classification task.
Option C, using a different optimizer, might not necessarily fix the issue and could lead to slower convergence or worse performance.
Therefore, option D, using some form of regularization, is the most appropriate solution to consider in this situation.
👍 3AjoseO2023/02/17 I'd go A. Regularization could not guarantee higher validation accuracy.
👍 2KlaudYu2022/07/04
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