Topic 1 Question 17
A company has petabytes of unlabeled customer data to use for an advertisement campaign. The company wants to classify its customers into tiers to advertise and promote the company's products. Which methodology should the company use to meet these requirements?
Supervised learning
Unsupervised learning
Reinforcement learning
Reinforcement learning from human feedback (RLHF)
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Because of the large amounts of unlabeled data and need to identify patterns or groupings within that data, Unsupervised learning is best. Clustering techniques can be used to classify customers into different tiers.
👍 7galliaj2024/11/01- 正解だと思う選択肢: B
B: Unsupervised learning
Explanation: Unsupervised learning is used when working with unlabeled data, such as the customer data described in this scenario. This methodology allows the company to identify patterns and group similar customers into clusters or tiers without the need for predefined labels. Techniques like clustering (e.g., K-Means or hierarchical clustering) would help classify customers based on shared characteristics for targeted advertisement campaigns.
Why not the other options? A: Supervised learning: Supervised learning requires labeled data, which is not available in this case. Labels would need to be provided for each customer, making this approach unsuitable for the given scenario.
👍 4Moon2024/12/30 - 正解だと思う選択肢: B
B. Unsupervised learning
👍 3jove2024/11/05
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