What technique is used for identifying unusual data points?

Anomaly detection is a technique specifically designed to identify unusual data points within a dataset. It involves analyzing data to recognize patterns and then flagging instances that deviate significantly from these patterns as anomalies or outliers. This process is crucial in various applications, such as fraud detection, network security, and fault detection, where identifying irregular behavior can prevent significant losses or highlight issues that require immediate attention.

In anomaly detection, methods such as statistical tests, clustering techniques, or machine learning algorithms can be employed to distinguish normal behavior from abnormal instances. By focusing on the characteristics of the data and establishing a baseline for what is considered "normal," this technique can effectively spot data points that are unexpected and warrant further investigation.

The other options, while important in their own right, do not directly address the identification of unusual data points. Predictive modeling focuses on forecasting outcomes based on historical data, data normalization involves adjusting the range of data values, and feature engineering pertains to selecting and transforming variables to improve model performance. Each plays a distinct role in the broader context of data analysis but does not specialize in detecting anomalies.