AI-900: Anomaly Detection Examples: Housing Prices, Sign-ins, and Diabetes Predictions

Explore real-world examples of anomaly detection, including forecasting housing prices, identifying suspicious sign-ins, and predicting diabetes based on medical history. Learn how anomaly detection plays a crucial role in various domains.

Question

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Statement 1: Forecasting housing prices based on historical data is an example of anomaly detection.
Statement 2: Identifying suspicious sign-ins by looking for deviations from usual patterns is an example of anomaly detection.
Statement 3: Predicting whether a patient will develop diabetes based on the patient’s medical history is an example of anomaly detection.

Answer

Statement 1: No
Statement 2: Yes
Statement 3: No

Explanation

Anomaly detection encompasses many important tasks in machine learning:

• Identifying transactions that are potentially fraudulent.
• Learning patterns that indicate that a network intrusion has occurred.
• Finding abnormal clusters of patients.
• Checking values entered into a system.

Statement 1: Forecasting housing prices based on historical data is an example of anomaly detection.

Answer: No. Forecasting housing prices based on historical data is an example of regression, which is a type of supervised learning that predicts a continuous numerical value based on input features. Anomaly detection is a type of unsupervised learning that identifies rare items, events, or observations that deviate significantly from the majority of the data and do not conform to a well-defined notion of normal behavior.

Statement 2: Identifying suspicious sign-ins by looking for deviations from usual patterns is an example of anomaly detection.

Answer: Yes. Identifying suspicious sign-ins by looking for deviations from usual patterns is an example of anomaly detection, which can be used for cyber security applications such as fraud detection, intrusion detection, and network monitoring. Anomaly detection can help detect global outliers (also called point anomalies) or contextual anomalies, which means that there is a departure from a set of data points in context.

Statement 3: Predicting whether a patient will develop diabetes based on the patient’s medical history is an example of anomaly detection.

Answer: No. Predicting whether a patient will develop diabetes based on the patient’s medical history is an example of classification, which is a type of supervised learning that predicts a discrete categorical value based on input features. Anomaly detection is a type of unsupervised learning that does not use labeled data to learn a model.

Reference

Microsoft Learn > Previous Versions > Module Categories and Descriptions > Machine Learning Modules > Initialize Model > Anomaly Detection

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