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Copula-based outlier detector (COPOD)
Citekey LiEtAl2020COPOD
Source Code https://github.com/yzhao062/pyod/blob/master/pyod/models/copod.py
Learning type unsupervised
Input dimensionality multivariate

Parameters

  • contamination: float in (0., 0.5), optional (default=0.1)
    The amount of contamination of the data set, i.e. the proportion of outliers in the data set. When fitting this is used to define the threshold on the decision function. Automatically determined by algorithm script!!

Citation format (for source code)

Zhao, Y., Nasrullah, Z. and Li, Z., 2019. PyOD: A Python Toolbox for Scalable Outlier Detection. Journal of machine learning research (JMLR), 20(96), pp.1-7.