AWS Certified AI PractitionerAI/ML and Generative AI FundamentalsEasy
A financial institution is implementing a machine learning model to detect fraudulent transactions. The model is designed to flag transactions that deviate significantly from a customer's typical spending patterns. This approach is an example of which type of machine learning task?
- AAnomaly Detection
- BClustering
- CClassification
- DRegression
Show answer & explanationAnswer & explanation
Correct answer: A. Anomaly Detection
Detecting transactions that 'deviate significantly from a customer's typical spending patterns' is a classic example of identifying outliers or anomalies. Anomaly detection is a machine learning task specifically designed for this purpose.
Why the other options are wrong
- B. Clustering groups similar data points together, but it doesn't specifically identify individual points as 'anomalous' based on deviation from a norm.
- C. Classification categorizes data into predefined classes, which is not the primary goal here (e.g., 'fraud' or 'not fraud' is the *result* of detecting an anomaly, not the detection itself).
- D. Regression predicts a continuous numerical value, which is not applicable to identifying unusual transactions.
Anomaly Detection (ML Task)
Anomaly detection is a machine learning task focused on identifying rare observations or events that deviate significantly from the majority of the data, often indicating a problem or unusual activity.
- Identifies outliers/deviations.
- Used for fraud, error, or intrusion detection.
- Often involves unsupervised or semi-supervised learning.
Memory trick: Models predict, categorize, group, or find oddities.