Description Usage Arguments Value References Examples
The eif
function is an R wrapper around the original Python implementation
of the Extended Isolation Forest (Hariri, Kind, Brunner (2018) <arXiv:1811.02141>) algorithm for
anomaly detection, developed by the original authors of the paper. This extension improves the
consistency and reliability of anomaly scores produced by the standard Isolation Forest
(Liu, Ting, Zhou (2008) <doi:10.1109/ICDM.2008.17>). Extended Isolation Forests allows
for the slicing of the data to be carried out using hyperplanes with random slopes which
results in improved score maps.
1  eif(X, ntrees, sample_size, ExtensionLevel)

X 
(matrix) A numeric data matrix. 
ntrees 
(integer) Number of trees to be used in fitting the forest. 
sample_size 
(integer) Number of rows to be subsampled in creating each tree. This must be less than the number of observations in the dataset. 
ExtensionLevel 
(integer) Degrees of freedom in choosing the hyperplanes
for dividing up the data. This must be less than the dimension of the dataset. Setting

A named list of length two containing the isolation forest (in iforest
)
and anomaly scores (in scores
).
Liu, Ting and Zhou. "Isolation Forest." IEEE International Conference on Data Mining (2008).
Hariri, Kind and Brunner. "Extended Isolation Forest." arXiv:1811.02141 (2018).
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