Isolation Forest

What it is

A distributed Spark/Scala implementation of the isolation forest algorithm designed for scalable training and ONNX export, enabling efficient cross-platform inference for unsupervised outlier detection tasks.

The only Spark-based implementation of isolation forest with built-in support for scalable training and ONNX export, making it ideal for large-scale unsupervised outlier detection tasks.

When it fits — and when it doesn’t

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

1. 01

   Limited language support due to primary Scala implementation

   The tool is primarily developed in Scala, which might limit its accessibility and integration for developers who are not familiar with or do not use Scala.

2. 02

   Complex setup process for distributed environments

   Setting up a distributed environment using Spark can be complex and requires significant infrastructure knowledge, which may pose challenges for teams without extensive experience in this area.

3. 03

   Performance issues with large datasets

   While designed to handle scalable training, performance degradation can occur when processing extremely large datasets due to the overhead of distributed computing and data shuffling across nodes.

What it does well

• Distributed implementation for scalable training

• Supports ONNX export for cross-platform inference

• Unsupervised outlier detection algorithm

Pricing

Free tier

YesOpen source — free to use

Paid from

$0

Model

Flat rate

Enterprise plan

No

Pricing not independently confirmed — check the vendor’s page.