Isolation Forest
Distributed Spark/Scala implementation for unsupervised outlier detection.
Pricing
See website
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
—Overview
What is Isolation Forest?
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.
Key differentiator
“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.”
Capability profile
Strength Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
Fit analysis
Who is it for?
✓ Best for
Teams working with Spark who need scalable outlier detection solutions
Projects requiring cross-platform inference capabilities via ONNX export
✕ Not a fit for
Applications that require real-time anomaly detection without batch processing
Scenarios where the overhead of distributed computing is not justified by dataset size
Cost structure
Pricing
Free Tier
None
Starts at
See website
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Next step
Get Started with Isolation Forest
Step-by-step setup guide with code examples and common gotchas.