Topic 1 Question 130
A data scientist must build a custom recommendation model in Amazon SageMaker for an online retail company. Due to the nature of the company's products, customers buy only 4-5 products every 5-10 years. So, the company relies on a steady stream of new customers. When a new customer signs up, the company collects data on the customer's preferences. Below is a sample of the data available to the data scientist.
How should the data scientist split the dataset into a training and test set for this use case?Shuffle all interaction data. Split off the last 10% of the interaction data for the test set.
Identify the most recent 10% of interactions for each user. Split off these interactions for the test set.
Identify the 10% of users with the least interaction data. Split off all interaction data from these users for the test set.
Randomly select 10% of the users. Split off all interaction data from these users for the test set.
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コメント(8)
I would select B, straight from this AWS example: https://aws.amazon.com/blogs/machine-learning/building-a-customized-recommender-system-in-amazon-sagemaker/
👍 22joep212021/09/19I think the answer is D because customers by only 4-5 products every 5-10 years so it doesn't make sense to get 10% interactions for each user as a test set.
👍 5NicZ11112021/11/01- 正解だと思う選択肢: B
I think it is a problem of leakage, so B is the correct answer https://www.datarobot.com/wiki/target-leakage/
👍 3gggsrs2022/01/07
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