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Isolation Forest for anomaly detection

Project description

# iso_forest

This is a simple package implementation for the isolation forest method described (among other places) in this [paper](icdm08b.pdf) for detecting anomalies and outliers from a data point distribution.

## Extended isolation forest

For an extended version of this algorithm that produces more precise scoring maps please visit this repository

[https://github.com/sahandha/eif](https://github.com/sahandha/eif)/

## Installation

pip install iso_forest

or directly from the Github repository

pip install git+https://github.com/mgckind/iso_forest.git

It supports python2 and python3

## Requirements

  • numpy

No extra requirements are needed for the algorithm.

In addition, it also contains means to draw the trees created using the [igraph](http://igraph.org/) library.

## Use Examples

See these 2 notebooks examples on how to use it

  • [basics](demo_iforest.ipynb)
  • [tree visualization and anomaly PDFs](demo_vis_pdf.ipynb)

## Releases

### v1.0.3

  • Initial Release

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