Example Concept: Anomaly detection models, such as Isolation Forest, One-Class SVM, or Autoencoders, are designed to identify rare events by learning the normal patterns of the data. These models can be trained on historical data to understand typical behavior and then used to flag deviations from this norm. Techniques like feature scaling, dimensionality reduction, and ensemble methods can improve the model's ability to detect anomalies by focusing on the most relevant features and reducing noise. Additionally, using evaluation metrics like precision-recall curves helps in assessing the model's performance in identifying true anomalies without being misled by the imbalance in the dataset.