Average Accuracy: 37.62
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Outlier Detection
Outlier Detection is a task of identifying a subset of a given data set which are considered anomalous in that they are unusual from other instances. It is one of the core data mining tasks and is central to many applications. In the security field, it can be used to identify potentially threatening users, in the manufacturing field it can be used to identify parts that are likely to fail.
<span class="description-source">Source: Coverage-based Outlier Explanation </span>
논문
AUC: 0.8642
Accuracy: 0.94
Average F1: 0.74
Accuracy: 0.9843
Accuracy: 0.9496
AUROC: 0.997