AIF-C01 Question 16
Single answerA retail company wants to build a machine learning model to predict whether a customer will purchase a product based on their browsing and purchase history. Which type of machine learning is best suited for this task?
- A
Supervised learning
- B
Unsupervised learning
- C
Reinforcement learning
- D
Deep learning
Show answer and explanation
Correct answer: A
Explanation
Supervised learning is the best approach when you have labeled data, as is the case in this scenario where historical customer data includes input features and corresponding labels (purchase or no purchase). Unsupervised learning is for finding patterns in unlabeled data, and reinforcement learning is for decision-making in dynamic environments. Deep learning is a broader methodology that can apply to different learning types, but it does not specifically address the type of learning required here.
- A. Correct.
Correct: Supervised learning is used when you have labeled data, such as historical data with input features (customer browsing behavior) and corresponding labels (whether the customer made a purchase). This makes it the best fit for this predictive task.
- B. Incorrect.
Incorrect: Unsupervised learning is used when the data is not labeled and you aim to discover hidden patterns or groupings. Predicting outcomes, like purchases, requires labeled data and is not suitable for unsupervised learning.
- C. Incorrect.
Incorrect: Reinforcement learning is used when an agent learns to make decisions by interacting with an environment and receiving rewards or penalties. This is not applicable to a task involving labeled historical data.
- D. Incorrect.
Incorrect: Deep learning is a subset of machine learning and can be used in supervised, unsupervised, or reinforcement learning tasks. However, the question asks for the type of learning, not a specific technique or algorithm.