AI engineering glossary · Classical ML algorithms
What is anomaly detection?
Anomaly detection (also called Outlier detection): Finding observations that differ strongly from the majority of data, without or with few labelled examples. It is used for fraud, faults and intrusion, and methods include isolation forests, density-based and distance-based techniques.
Anomaly detection: an example
Flagging a card transaction of 3 lakh from a new country on an account that usually spends under 5,000.
Related terms
More Classical ML algorithms terms
- Apriori algorithm
- Association rule learning
- Bagging
- Bayesian network
- Bernoulli naive Bayes
- Boosting
- Bootstrap sampling
- C4.5
Where you learn it
Anomaly detection is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.