AIF-C01 Question 18
Single answerA data science team at a retail company wants to build a machine learning model to recommend products to customers based on their purchase history. They are considering different approaches. Which type of learning is most suitable if the dataset contains customer purchase labels (e.g., 'Purchased' or 'Not Purchased')?
- A
Supervised learning
- B
Unsupervised learning
- C
Reinforcement learning
- D
Semi-supervised learning
Show answer and explanation
Correct answer: A
Explanation
The scenario describes a dataset with labeled outcomes ('Purchased' or 'Not Purchased'), which makes supervised learning the most suitable approach. Supervised learning uses labeled data to train models for classification or regression tasks. In contrast, unsupervised learning works on unlabeled data, reinforcement learning focuses on decision-making through interaction with an environment, and semi-supervised learning is used when only part of the dataset is labeled.
- A. Correct.
Supervised learning is the correct choice because the dataset contains labeled examples (e.g., 'Purchased' or 'Not Purchased'), which are used to train the model to make predictions.
- B. Incorrect.
Unsupervised learning is not suitable in this case because it is used when the data lacks labels, focusing on finding patterns or groupings in data without defined outcomes.
- C. Incorrect.
Reinforcement learning is not appropriate here because it is used in scenarios where an agent learns by interacting with an environment to maximize rewards, which is not applicable to this problem.
- D. Incorrect.
Semi-supervised learning is not the best choice as it is used when the dataset consists of a mix of labeled and unlabeled data, but in this scenario, the dataset is fully labeled.