Observability & MonitoringSAGE
Global feature importance calculation using Shapley values.
- Status
- Not yet determined
- Last checked
- Aging · Sep 3, 2026
- License
- Open source
- Pricing
- Free tier · from $0
What it is
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.
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.
When it fits — and when it doesn’t
A good fit for
Teams working on interpretable machine learning who need global feature importance calculations.
Projects requiring detailed explanations of model behavior and feature impact.
Look elsewhere if
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.
Known weaknesses
Each one links to the public issue, changelog or docs page it came from, where we have one.
- 01
Performance issues with large datasets
Calculating Shapley values can be computationally expensive and slow for high-dimensional data
What it does well
Calculates global feature importance using Shapley values.
Provides insights into the impact of features on model predictions.
Open-source and MIT licensed.
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.
Performance benchmarks
How Fast Is It?
Set up SAGE
Setup steps, common gotchas and what to check after 30 days.