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https://issues.apache.org/jira/browse/SPARK-22867?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16301498#comment-16301498
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Sean Owen commented on SPARK-22867:
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The problem is that this goes for a hundred things. I don't think MLlib aspires
to be like scikit, especially because you can easily add a third-party package
to any app. It's just the basics. Given that most JIRAs like this have been
rejected I doubt this woudl be different. At least you'd need to argue this is
widely used (e.g. papers, other implementations)
> Add Isolation Forest algorithm to MLlib
> ---------------------------------------
>
> Key: SPARK-22867
> URL: https://issues.apache.org/jira/browse/SPARK-22867
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Affects Versions: 2.2.1
> Reporter: Fangzhou Yang
>
> Isolation Forest (iForest) is an effective model that focuses on anomaly
> isolation.
> iForest uses tree structure for modeling data, iTree isolates anomalies
> closer to the root of the tree as compared to normal points.
> A anomaly score is calculated by iForest model to measure the abnormality of
> the data instances. The lower, the more abnormal.
> More details about iForest can be found in the following papers:
> <a href="https://dl.acm.org/citation.cfm?id=1511387";>Isolation Forest</a> [1]
> and <a href="https://dl.acm.org/citation.cfm?id=2133363";>Isolation-Based
> Anomaly Detection</a> [2].
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