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Anomaly detection

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TL;DR. Anomaly detection identifies unusual data points that deviate significantly from the expected pattern.

Technical Definition

Anomaly detection identifies unusual data points that deviate significantly from the expected pattern.

How it works

Anomaly detection is the process of finding data points, events, or observations that deviate significantly from the norm or expected behavior of a dataset. These outliers, often called anomalies, can indicate errors, rare events, or emerging trends that require further investigation.

Related Concepts

  • Unsupervised Learning — Learning patterns from data that has no labels — only the inputs.
  • Data mining — Discovering patterns and insights in large datasets using methods from machine learning, statistics, and databases.

Further Reading

  • Google ML Glossary