DS-301h · Module 1

ML-Based Anomaly Detection

3 min read

ML-based detection learns what "normal" looks like from historical data and flags deviations from the learned pattern. Isolation forests detect anomalies by randomly partitioning the data — anomalies are isolated in fewer partitions because they are different from the majority. Autoencoders learn to compress and reconstruct normal data — anomalies produce high reconstruction error because they do not match the learned pattern. LSTM networks learn temporal patterns and flag when the sequence deviates from expectation. Each method handles different types of anomalies. Isolation forests excel at multivariate anomalies (unusual combinations of values). Autoencoders excel at complex pattern anomalies. LSTMs excel at temporal sequence anomalies.

Do This

  • Use ML-based detection for multivariate anomalies where statistical methods fall short
  • Train on a clean historical dataset — if the training data contains anomalies, the model learns them as normal
  • Validate detection performance on a labeled holdout set before deploying to production

Avoid This

  • Default to ML-based detection without trying statistical methods first — simpler methods are easier to interpret and debug
  • Deploy an ML anomaly detector without explaining what it considers "normal" — uninterpretable detection erodes trust
  • Assume ML detection eliminates false positives — it reduces them, but tuning is still required