Question

A retail analytics team wants to build a model that predicts whether a customer will churn based on historical account data. They have three years of records where each customer is already tagged as 'churned' or 'retained'. A data scientist argues this is a classic supervised learning problem. Which characteristic of the team's dataset most directly justifies classifying this as supervised learning?

Accepted Answer

The dataset contains labeled examples with known churn outcomes used to train the model to map inputs to correct outputs

Answer

The dataset is large enough to require GPU acceleration for training

Answer

The model must group customers into clusters without any predefined categories

Answer

The model learns by receiving rewards and penalties as it interacts with a live environment