Observability & Monitoring

Shapash

Python library for interpretable machine learning visualizations

Status
Generally available
Last checked
Checked · Oct 12, 2026
License
Open source
Pricing
Free tier · from $0
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What it is

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.

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.

When it fits — and when it doesn’t

A good fit 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

Look elsewhere if

  • Real-time applications requiring low-latency responses from visualizations

  • Use cases that require highly specialized visualization techniques not covered by Shapash

Known weaknesses

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

  1. 01

    Limited integrations with non-Python ML libraries

    Primary support is for Python-based ML frameworks like scikit-learn and TensorFlow, lacking native support for R or Julia models

What it does well

  • Interpretable visualizations for machine learning models

  • Explicit labels that everyone can understand

  • Integration with popular Python ML libraries

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

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Set up Shapash

Setup steps, common gotchas and what to check after 30 days.

Open the setup guide →
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