| Citekey | GoldsteinDengel2012Histogrambased |
| Source Code | https://github.com/yzhao062/pyod/blob/master/pyod/models/hbos.py |
| Learning type | unsupervised |
| Input dimensionality | multivariate |
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n_bins: int, optional (default=10)
The number of bins. -
alpha: float in (0, 1), optional (default=0.1)
The regularizer for preventing overflow. -
tol: float in (0, 1), optional (default=0.5)
The parameter to decide the flexibility while dealing the samples falling outside the bins. -
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.