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Maria Karanasou
spark-iforest
Commits
34e7a1e3
Unverified
Commit
34e7a1e3
authored
Mar 01, 2019
by
Yang, Fangzhou
Committed by
GitHub
Mar 01, 2019
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Update README.md
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@@ -23,6 +23,8 @@ It is implemented in the following steps:
Spark iForest is designed and implemented easy to use. The usage is similar to the iForest sklearn implementation [3].
In addition, pyspark package is also provided. More details and usage can be found in python folder.
*Parameters:*
-
*numTrees:*
The number of trees in the iforest model (>0).
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*predictionCol:*
Prediction column name, default "prediction".
## Examples
The following codes are an example for detecting anamaly data points using
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If you encounter any bugs, feel free to submit an issue or pull request. Also you can email to:
<a href="fangzhou.yang@hotmail.com">Yang, Fangzhou (fangzhou.yang@hotmail.com)</a>
## Citation
Please cite spark-iforest in your publications if it helped your research. Here is an example BibTeX entry:
```
@misc{titicacasparkiforest,
title={spark-iforest},
author={Yang, Fangzhou and contributors},
year={2018},
publisher={GitHub},
howpublished={\url{https://github.com/titicaca/spark-iforest}},
}
```
## References:
[1] Liu F T, Ting K M, Zhou Z, et al. Isolation Forest[C]. international conference on data mining, 2008.
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