Answer: Supervised Learning

## Explanation **Supervised Learning** is the correct approach because: 1. **Labeled Data**: The provider has "millions of examples of emails already tagged accordingly" - this means they have labeled training data where each email is classified as spam or not spam. 2. **Classification Task**: The goal is to classify emails into two categories (spam/not spam), which is a classic supervised learning classification problem. 3. **Why not other options**: - **Unsupervised Learning (A)**: Used when there are no labels; the algorithm finds patterns in unlabeled data. - **Reinforcement Learning (B)**: Used for decision-making problems where an agent learns through trial and error with rewards/penalties. - **Clustering (D)**: A type of unsupervised learning that groups similar data points together without predefined labels. **Key Concept**: Supervised learning algorithms learn from labeled training data to make predictions or decisions on new, unseen data. With millions of labeled examples, a supervised learning model (like logistic regression, decision trees, or neural networks) can be trained to accurately classify emails as spam or not spam.

Author: Jin H