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        "author": "J. Marcus Hughes",
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        "description": "# Combination Robust Cut Forests\n[![CodeFactor](https://www.codefactor.io/repository/github/jmbhughes/crcf/badge)](https://www.codefactor.io/repository/github/jmbhughes/crcf)\n[![PyPI version](https://badge.fury.io/py/crcf.svg)](https://badge.fury.io/py/crcf)\n\nIsolation Forests **[Liu+2008]** and Robust Random Cut Trees **[Guha+2016]** are very similar in many ways, \nas outlined in the [supporting overview](overview.pdf). Most notably, they are extremes\nof the same outlier scoring function: \n\n![equation](https://latex.codecogs.com/gif.latex?%5Ctext%7BFor%20%7D%20%5Ctheta%20%5Cin%20%5B0%2C1%5D%20%5Ctext%7B%20let%20%7D%20%5Cmathrm%7Bscore%7D%28x%29%20%3D%20%5Ctheta%20%5Cmathrm%7Bdepth%7D%28x%29%20+%20%281-%5Ctheta%29%5Cmathrm%7Bdisp%7D%28x%29)\n\nThe combination robust cut forest allows you to combine both scores by using an theta other than 0 or 1. \n\n# Install\nDownload the repository and run \n`python3 setup.py install` or `pip3 install .`\n\nThe tests can be run from `pytest` with `python3 setup.py test`.\n\n# Tasks\n- [ ] complete basic implementation\n- [ ] fix documentation generation error\n- [ ] provide clear documentation and usage instructions\n- [ ] incorporate categorical variable support, including categorical rules\n- [ ] complete the write-up document with a benchmarking of performance\n\n# References\n- **[Liu+2008]**: [Liu, Fei Tony, Kai Ming Ting, and Zhi-Hua Zhou. \n\"Isolation forest.\" In 2008 Eighth IEEE International Conference on Data Mining, \npp. 413-422. IEEE, 2008.](https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf?q=isolation-forest)\n- **[Guha+2016]**: [Guha, Sudipto, Nina Mishra, Gourav Roy, and Okke Schrijvers. \n\"Robust random cut forest based anomaly detection on streams.\" \nIn International conference on machine learning, pp. 2712-2721. 2016.](http://proceedings.mlr.press/v48/guha16.pdf)\n\n\n",
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