MLS-C01 Question 135
Single answerA data scientist is tasked with analyzing a dataset containing customer transaction records to identify patterns in customer purchasing behavior. The dataset does not include any labels or target variables. Which type of machine learning approach should the data scientist use for this task?
- A
Supervised learning
- B
Unsupervised learning
- C
Reinforcement learning
- D
Semi-supervised learning
Show answer and explanation
Correct answer: B
Explanation
The task involves analyzing an unlabeled dataset to find patterns, which is a hallmark use case for unsupervised learning. Supervised learning and semi-supervised learning require labeled data, while reinforcement learning is used for sequential decision-making problems. Therefore, unsupervised learning is the most suitable approach for this scenario.
- A. Incorrect.
Supervised learning requires labeled data with input-output pairs (features and target variables). Since the dataset in the scenario is unlabeled, supervised learning is not appropriate.
- B. Correct.
Unsupervised learning is the correct approach for analyzing unlabeled datasets. It is used to identify patterns, groupings, or structures in the data, which aligns with the task described in the scenario.
- C. Incorrect.
Reinforcement learning is used for decision-making tasks where an agent interacts with an environment and learns through rewards or penalties. This approach is not relevant to the problem described in the scenario.
- D. Incorrect.
Semi-supervised learning is a hybrid approach that leverages both labeled and unlabeled data to improve learning. Since the dataset is entirely unlabeled, this approach is not applicable here.