Shapash
Python library for interpretable machine learning visualizations
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is Shapash?
Shapash is a Python library that provides several types of visualization to display explicit labels everyone can understand, making it easier to interpret and explain machine learning models.
Key differentiator
“Shapash stands out for its focus on making machine learning models interpretable and understandable to a wide audience, offering clear visual explanations of model predictions.”
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 support is for Python-based ML frameworks like scikit-learn and TensorFlow, lacking native support for R or Julia models
GitHub issues have low activity and infrequent responses from maintainers
Fit analysis
Who is it for?
✓ Best for
Teams needing clear explanations of ML predictions for regulatory or compliance reasons
Developers looking to improve the interpretability and trustworthiness of their models
Projects where model transparency is critical, such as healthcare or finance
✕ Not a fit for
Real-time applications requiring low-latency responses from visualizations
Use cases that require highly specialized visualization techniques not covered by Shapash
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
Alternatives
Works well with
Next step
Get Started with Shapash
Step-by-step setup guide with code examples and common gotchas.