Anomaly Detection with H2O

What is anomaly detection?

Anomaly detection, also known as outlier detection, is a technique used in data analysis and machine learning to identify data points or instances that deviate significantly from the expected behavior of the majority of the data. These anomalous data points are referred to as anomalies or outliers.

The goal of anomaly detection is to distinguish between normal and abnormal patterns in the data. Anomaly detection is crucial for detecting rare events or abnormal behaviors that may have important implications for decision-making, risk assessment, and quality control. It’s commonly used in domains such as finance, cybersecurity, manufacturing, and healthcare to identify unusual or suspicious events or patterns that may indicate errors, fraud, faults, ...