| Citekey | TangEtAl2002Enhancing |
| Source Code | https://github.com/yzhao062/pyod/blob/master/pyod/models/cof.py |
| Learning type | unsupervised |
| Input dimensionality | multivariate |
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n_neighbors:int, optional (default=20)
Number of neighbors to use by default for k neighbors queries. Note that n_neighbors should be less than the number of samples. If n_neighbors is larger than the number of samples provided, all samples will be used. -
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!!
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.