SAGE
Global feature importance calculation using Shapley values.
Pricing
Free tier
Flat rate
Adoption
↘CoolingLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is SAGE?
SAGE is a tool for calculating global feature importance using Shapley values, providing insights into the impact of features on model predictions. It's essential for understanding and explaining complex models in machine learning projects.
Key differentiator
“SAGE stands out by offering a robust method for calculating global feature importance using Shapley values, which provides a deeper level of insight into the impact of features on model predictions compared to other methods.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns, TypeScript SDK is community-maintained
v0.1 to v0.2 migration required rewriting chain definitions
Primary development and documentation focus on Python, with minimal official support for other languages
Calculating Shapley values can be computationally expensive and slow for high-dimensional data
Fit analysis
Who is it for?
✓ Best for
Teams working on interpretable machine learning who need global feature importance calculations.
Projects requiring detailed explanations of model behavior and feature impact.
✕ Not a fit for
Real-time applications where quick computation is critical, as Shapley value calculation can be computationally expensive.
Applications that do not require a deep understanding of feature importance or model interpretability.
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Works well with
Next step
Get Started with SAGE
Step-by-step setup guide with code examples and common gotchas.