Explainerdashboard

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Explainerdashboard is an Open Source AI tool that quickly builds Explainable AI dashboards for machine learning models. It provides interactive plots for model performance, feature importances, and individual prediction contributions.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is explainerdashboard?

Explainerdashboard is an open-source Python package designed to quickly build Explainable AI dashboards for 'blackbox' machine learning models. It offers interactive plots to visualize model performance, feature importances, feature contributions to individual predictions, and 'what if' analysis. The tool supports various models including scikit-learn, xgboost, catboost, lightgbm, and skorch. Users can explore components in a notebook environment, design custom layouts, or combine multiple dashboards into an ExplainerHub. Dashboards can be exported to static HTML, making it easy to share insights and integrate into CI/CD processes.

Best used for

Ideal for data scientists and developers who need to quickly build interactive dashboards to explain machine learning model predictions, visualize model performance, and analyze feature importances. Especially valuable for organizations requiring transparency and interpretability for internal stakeholders or external regulators.

Common actions

explain AI models

visualize model performance

interpret machine learning

build interactive dashboards

analyze feature importance

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Capabilities

Key features

Interactive plots
SHAP values
Permutation importances
Partial dependence plots
Decision tree visualization
Customizable dashboards
Static HTML export

Target Audience

data scientistdeveloperproduct manager

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What machine learning models does Explainerdashboard support?

Explainerdashboard is compatible with a wide range of machine learning models, including scikit-learn, xgboost, catboost, lightgbm, and skorch (a scikit-learn wrapper for tabular PyTorch models). This broad compatibility allows it to be used with many common 'blackbox' models.

Can I customize the layout and content of the dashboards?

Yes, the library is designed to be modular, allowing users to design their own interactive dashboards with plotly dash. This enables customization of layout and explanations to suit specific project needs or target audiences, such as business users.

How can Explainerdashboard help with slow calculations for large datasets?

To mitigate slow calculations, users can switch off the interactions tab, disable permutation importances, calculate approximate SHAP values, or use GPU Tree SHAP if supported. Additionally, storing and loading explainer objects can prevent recalculation of properties.

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