linkedin/isolation-forest
A distributed Spark/Scala implementation of the isolation forest algorithm for unsupervised outlier detection, featuring support for scalable training and ONNX export for easy cross-platform inference.
This project helps data scientists and machine learning engineers detect unusual patterns or fraudulent activities within very large datasets. You input your large, numerical datasets, and it helps identify the data points that deviate significantly from the norm. This is particularly useful for those working with massive amounts of data in distributed computing environments.
253 stars.
Use this if you need to find anomalies or outliers in extremely large datasets using distributed computing frameworks like Apache Spark.
Not ideal if you are working with smaller datasets or prefer solutions that don't require a distributed processing setup.
Stars
253
Forks
54
Language
Scala
License
—
Category
Last pushed
Mar 12, 2026
Commits (30d)
0
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