In your recent AI project, was it an anomaly detection problem or a classification problem?

Anomaly detection and classification are both supervised learning tasks, but they differ in the nature of the labels and the goal. In classification, each instance is assigned to one of a fixed set of classes, and the training data contains examples of every class. The model learns decision boundaries that separate the classes. In anomaly detection, the majority of the data belongs to a normal class, and anomalies are rare or even unseen during training. The goal is to flag instances that deviate significantly from the normal pattern. Techniques for anomaly detection include statistical methods (e.g., z‑score), distance‑based methods (e.g., k‑NN), and model‑based methods (e.g., autoencoders). In contrast, classification often uses algorithms like logistic regression, decision trees, or deep neural networks trained with cross‑entropy loss. In an AI project, you would choose anomaly detection if you need to detect fraud, intrusion, or equipment failures where abnormal events are rare. You would choose classification if you need to label emails as spam or not, or classify images into categories. Understanding the data distribution, label availability, and business impact helps decide which approach to use.