A subscription-based fitness app wants to reduce customer churn, and the analytics team has historical data showing which past customers stayed or canceled. They want to train a model that can predict, for each current user, whether they are likely to churn next month based on their labeled history.

What type of learning problem is this?

Question

A subscription-based fitness app wants to reduce customer churn, and the analytics team has historical data showing which past customers stayed or canceled. They want to train a model that can predict, for each current user, whether they are likely to churn next month based on their labeled history.

What type of learning problem is this?

BlackTom AI · Accepted Answer

In this scenario, the goal is to predict a future outcome (whether a current user will churn next month) using historical data that includes labels indicating whether past customers stayed or canceled.

Option 1: Dimensionality Reduction. This technique reduces the number of features in the data, often used for visualization or to simplify models, but it does not by itself produce predictive outputs based on labeled examples. Here, there is a clear target we want to predict, so dimensionality reduction alone is not the primary learning paradigm.

Option 2: Supervised Learning. This approach uses labeled historical data (features describing users and a target label such as churn/no churn) to train a model that can predict the label for new, unseen instances. Since the question describes labeled past customers and a goal to predict churn for current users, this is a textbook case of supervised learning.

Option 3: Unsupervised Learning. Unsupervised methods seek patterns or structure in data without provided labels (e.g., clustering, association). There is an explicit label indicating churn in the historical data, so unsupervised learning does not fit the objective of predicting a defined target outcome.

Option 4: Reinforcement Learning. RL involves an agent learning to make decisions through interactions with an environment and receiving feedback via rewards. It is not typically used for predicting a static label like churn based on historical labeled data, making this option inappropriate for the described task.

Overall, the scenario centers on learning from labeled historical outcomes to forecast a future label, which aligns with supervised learning principles, while the other options describe different learning paradigms that do not match the task at hand.

IS 4487-006 Fall 2025 Final Exam December 10 from 10:30 to 12:30

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