A company wants to group customers into classes indicating whether they will churn within the next six months. The dataset is labeled with churn/non-churn outcomes. What type of machine learning model should be used?

Classification is the correct choice because the problem involves predicting a discrete category (churn or non-churn). This is a supervised learning task where the model learns from labeled data to assign new data points to one of several predefined classes. Linear regression is incorrect because it predicts continuous numerical values, not discrete categories. Clustering is an unsupervised learning technique used to group unlabeled data points based on similarity, which is not the goal here as the data is labeled. Reinforcement learning involves an agent learning through trial and error in an environment to maximize a reward, which is not applicable to this predictive modeling problem.