Welcome to crcf
This is the crcf
package that unites Robust Cut Forests and Isolation Forests as
combination robust cut forests.
Isolation Forests [Liu+2008] and Robust Random Cut Trees [Guha+2016] are very similar in many ways, as outlined in the supporting overview. Most notably, they are extremes of the same outlier scoring function. The combination robust cut forest allows you to combine both scores by using a \(\theta\) other than 0 or 1.
\[
\theta \textrm{Depth} + (1 - \theta) \textrm{[Co]Disp}
\]
For a full walkthrough of the mathematics behind these forests, please see the overview.
References
- [Liu+2008]: Liu, Fei Tony, Kai Ming Ting, and Zhi-Hua Zhou. "Isolation forest." In 2008 Eighth IEEE International Conference on Data Mining, pp. 413-422. IEEE, 2008.
- [Guha+2016]: Guha, Sudipto, Nina Mishra, Gourav Roy, and Okke Schrijvers. "Robust random cut forest based anomaly detection on streams." In International conference on machine learning, pp. 2712-2721. 2016.