Anomaly Detection

#Learning Objectives

• Implement Z-score, IQR, and Isolation Forest anomaly detection methods from scratch
• Distinguish between point, contextual, and collective anomalies and select the appropriate detection method for each
• Explain why anomaly detection is framed as modeling normal data rather than classifying anomalies
• Compare unsupervised anomaly detection with supervised classification and evaluate the tradeoff between novel anomaly coverage and precision