ELI5
ELI5: A free, self-hosted Python library for feature importance, predictions, and model inspection. Ranked #20 of 29 in AI Model Explainability Software by our editors (6.3/10); pricing: Free plan; best for scikit-learn users explaining features and predictions.
At a glance
- Editor score6.3 / 10
- PricingFree plan
- Best forScikit-learn users explaining features and predictions
- Free planYes
- Paid fromNone
- Feature attributionYes
- Facts checked28 Sep 2026
Where it wins
- Explains weights, feature importance, and individual predictions.
- Supports permutation importance and LIME for black-box estimators.
- Inspects scikit-learn pipelines and highlights text contributions.
Where it doesn't
- A Python library, not a hosted explainability service.
- No counterfactuals, fairness analysis, or audit reports.
- Support is through documentation and community.
Our verdict on ELI5
ELI5 is an open-source Python library for practitioners who want to visualize, debug, and explain machine-learning models in their own environment. Its strongest fit is for users of scikit-learn and related libraries who need to inspect feature importance or understand individual predictions. It is a developer tool rather than a hosted service, and it runs on Linux, Windows, and macOS.
Its explanation tools cover both model-level and prediction-level questions. ELI5 can explain model weights and feature importance, compute permutation importance for black-box estimators, and use LIME to explain black-box text classifiers. It can also visualize decision trees as text or images, highlight text tokens by their contribution to a prediction, and inspect scikit-learn pipelines and feature transformations. The documented integrations include scikit-learn, XGBoost, LightGBM, CatBoost, lightning, sklearn-crfsuite, OpenAI, and Keras, giving Python teams several model ecosystems to work with through one library.
Because ELI5 is open source and free, there are no paid tiers to weigh; installation and use happen in the user's Python environment rather than through a hosted deployment. Documentation and community are the listed support channels. Its focus is explanation and inspection: counterfactuals, fairness analysis, and audit reports are outside its listed capabilities. Choose ELI5 if you want a code-based way to inspect supported estimators, feature contributions, or predictions, especially in a scikit-learn workflow. Teams seeking a hosted service or a broader set of governance and explanation functions should consider another product.
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ELI5 pricing
ELI5 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 estimators; XGBoost; LightGBM; CatBoost; lightning; sklearn-crfsuite; Keras; OpenAI models |
| Deployment | Self-hosted |
| Platforms | Linux, Windows, macOS |
| Support | Docs, Community |
| Built for | Solo, Small business, Mid-market, Enterprise (editorial estimate) |
| Integrations | 8 integrations: scikit-learn, XGBoost, LightGBM, CatBoost, lightning, sklearn-crfsuite … |
| Pricing | Free plan |
| Website | eli5.readthedocs.io |
| Facts checked | 28 Sep 2026 |
ELI5 integrations
ELI5 lists 8 integrations on its own site.
- scikit-learn
- XGBoost
- LightGBM
- CatBoost
- lightning
- sklearn-crfsuite
- OpenAI
- Keras
Alternatives to ELI5
- 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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