PDPbox
PDPbox: A free, focused toolkit for partial-dependence and ICE visualizations. Ranked #18 of 29 in AI Model Explainability Software by our editors (6.5/10); pricing: Free plan; best for teams focused on partial-dependence and ICE plots.
At a glance
- Editor score6.5 / 10
- PricingFree plan
- Best forTeams focused on partial-dependence and ICE plots
- Free planYes
- Paid fromNone
- Feature attributionYes
- Facts checked28 Sep 2026
Where it wins
- Plots single features, feature interactions, and ICE lines
- Supports multiclass models and target or prediction information plots
- Works with scikit-learn models or custom prediction functions
Where it doesn't
- Specializes in profile visualizations rather than broad explainability workflows
- Requires Python installation and self-hosted use
- Repository notes a lapse in maintenance beginning in 2018
Our verdict on PDPbox
PDPbox is an open-source Python toolbox for developers and data practitioners who want to visualize how features influence predictions from supervised machine-learning models. It focuses on partial dependence and related profile visualizations rather than serving as a hosted explainability platform. The published details describe pip and GitHub installation, with support across Linux, macOS, and Windows. Its free, self-hosted approach can suit solo practitioners through enterprise teams that are comfortable working in Python.
The feature set centers on inspecting feature effects: PDPbox can create partial dependence plots for individual features and feature interactions, plus individual conditional expectation (ICE) lines. It also supports multiclass models and target or prediction information plots. Standard scikit-learn models are detected automatically; other model interfaces can be used by supplying a custom prediction function. That makes it a fit when the main question is how predicted outcomes vary across feature values, including interactions, rather than a need for an all-purpose model governance workflow.
PDPbox’s narrow scope is also its key tradeoff. The category details mark feature attribution support, but not counterfactuals, fairness analysis, or audit reports. It is a Python package rather than a hosted service, and documentation is its listed support channel. The repository notes a lapse in maintenance beginning in 2018, alongside a stated intention to resume work. Choose PDPbox if partial-dependence and ICE plots are the priority and a self-hosted Python workflow fits; consider another tool if you need broader analysis capabilities or a hosted product.
Your product? Our editors can test PDPbox in depth and publish a review, labelled as paid · from $99/mo →
PDPbox pricing
PDPbox fact sheet
| Free plan | Yes |
|---|---|
| Paid from | None |
| Feature attribution | Yes |
| Counterfactual explanations | No |
| Fairness analysis | No |
| Audit reports | No |
| Deployment options | Self_hosted |
| Supported model types | scikit-learn models; other models via a custom prediction function |
| Deployment | Self-hosted |
| Platforms | Linux, macOS, Windows |
| Support | Docs |
| Built for | Solo, Small business, Mid-market, Enterprise (editorial estimate) |
| Pricing | Free plan |
| Website | github.com |
| Facts checked | 28 Sep 2026 |
Alternatives to PDPbox
- AIX360A broad, self-hosted Python toolkit for local, global, and time-series model explanations.9.1
- Arize AIProduction-focused explainability pairs feature attribution with fairness monitoring and model comparisons.8.5
- Fiddler AIA tracing and evaluation platform for teams debugging LLM and agent workflows.7.4
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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