Topic 1 Question 88
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?
Increase the randomization of training data in the mini-batches used in training
Allocate a higher proportion of the overall data to the training dataset
Apply L1 or L2 regularization and dropouts to the training
Reduce the number of layers and units (or neurons) from the deep learning network
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コメント(11)
I think C will be answer, because we even don't know how many layers now, so apply L1,L2 and dropouts layer will be first resort to solve overfitting. If it still does not work, then to reduce layers
👍 10knightknt2022/04/20- 正解だと思う選択肢: C
C is the answer
👍 4Abdelrahman_Omran2022/04/26 - 正解だと思う選択肢: D
Deep learning tuning order:
- Number of layers
- Number of neurons (indirectly implements dropout)
- L1/L2 regularization
- Dropout
👍 4Peeking2022/12/09
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