AIF-C01 · Question #176
A company is developing an ML application. The application must automatically group similar customers and products based on their characteristics. Which ML strategy should the company use to meet…
The correct answer is A. Unsupervised learning. The company needs to automatically group similar customers and products based on their characteristics, which is a clustering task. Unsupervised learning is the ML strategy for grouping data without labeled outcomes, making it ideal for this requirement. Unsupervised learning…
Question
A company is developing an ML application. The application must automatically group similar customers and products based on their characteristics. Which ML strategy should the company use to meet these requirements?
Options
- AUnsupervised learning
- BSupervised learning
- CReinforcement learning
- DSemi-supervised learning
How the community answered
(18 responses)- A78% (14)
- C17% (3)
- D6% (1)
Explanation
The company needs to automatically group similar customers and products based on their characteristics, which is a clustering task. Unsupervised learning is the ML strategy for grouping data without labeled outcomes, making it ideal for this requirement. Unsupervised learning is used to identify patterns or groupings in data without labeled outcomes. Common applications include clustering, such as grouping similar customers or products based on their characteristics, using algorithms like K-means or hierarchical clustering.
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