Anomaly detection: Difference between revisions
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In machine learning terms, '''anomaly detection''' refers to identifying suspicious elements within a given stream of data. | In machine learning terms, '''anomaly detection''' refers to identifying suspicious elements within a given stream of data. It finds outliers in a collection of datapoints. | ||
Anomaly detection is an example of [[unsupervised learning]] where the algorithm has to spot the element that doesn't fit with the group. | |||
== Use == | |||
Banks use AD to find fraudulent transactions. | |||
== See also == | |||
*[[Outlier]] | |||
== External links == | |||
* [https://www.youtube.com/watch?v=086OcT-5DYI Anomaly Detection Problem] [[wikipedia:Andrew Ng|Andrew Ng]] | |||
Latest revision as of 23:40, 3 May 2022
In machine learning terms, anomaly detection refers to identifying suspicious elements within a given stream of data. It finds outliers in a collection of datapoints.
Anomaly detection is an example of unsupervised learning where the algorithm has to spot the element that doesn't fit with the group.
Use
Banks use AD to find fraudulent transactions.