MLS-C01 Question 133
Single answerA data scientist at a retail company is tasked with building a machine learning model to segment customers based on their purchasing behavior. The dataset includes features such as purchase frequency, average spending, and preferred product categories, but there are no labels indicating predefined customer groups. Which type of machine learning approach should the data scientist use?
- A
Supervised learning
- B
Unsupervised learning
- C
Reinforcement learning
- D
Semi-supervised learning
Show answer and explanation
Correct answer: B
Explanation
The correct answer is 'Unsupervised learning' because the task involves finding patterns or clusters in an unlabeled dataset. Supervised learning requires labeled data, semi-supervised learning requires a mix of labeled and unlabeled data, and reinforcement learning is used for agent-environment interaction tasks, none of which apply here.
- A. Incorrect.
Supervised learning requires labeled data where the target variable is known. In this case, there are no predefined labels for customer groups, so supervised learning is not appropriate.
- B. Correct.
Unsupervised learning is used when there are no labels in the dataset, and the goal is to find hidden patterns or groupings. Since the task involves segmenting customers without predefined groups, unsupervised learning is the correct choice.
- C. Incorrect.
Reinforcement learning is typically used in environments where an agent learns by interacting with the environment and receiving rewards or penalties. This scenario does not involve such an interaction model, so reinforcement learning is not suitable.
- D. Incorrect.
Semi-supervised learning is used when the dataset contains a mix of labeled and unlabeled data. Since there are no labels at all in this scenario, semi-supervised learning is not applicable.