Extended Isolation Forest for anomaly detection
Project description
# Extended Isolation Forest
This is a simple package implementation for the Extended Isolation Forest method. It is an improvement on the original algorithm Isolation Forest which is described (among other places) in this [paper](icdm08b.pdf) for detecting anomalies and outliers from a data point distribution. The original code can be found at [https://github.com/mgckind/iso_forest](https://github.com/mgckind/iso_forest)
For an N dimensional data set, Extended Isolation Forest has N levels of extension, with 0 being identical to the case of standard Isolation Forest, and N-1 being the fully extended version.
## Installation
pip install eif
or directly from the repository
pip install git+https://github.com/sahandha/eif.git
## Requirements
numpy
No extra requirements are needed. In addition, it also contains means to draw the trees created using the [igraph](http://igraph.org/) library. See the example for tree visualizations
## Use
See these notebooks for examples on how to use it
[Basics](Notebooks/IsolationForest.ipynb)
[3D Example](Notebooks/general_3D_examples.ipynb)
[Tree visualizations](Notebooks/TreeVisualization.ipynb)
## Release
### v1.0.0 #### 2018-JUL-15 - Initial Release
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