SHAP
SHAP: A free, self-hosted Python library for feature attribution across varied model types. Ranked #9 of 29 in AI Model Explainability Software by our editors (7.4/10); pricing: Free plan; best for teams needing Shapley explanations across model types.
At a glance
- Editor score7.4 / 10
- PricingFree plan
- Best forTeams needing Shapley explanations across model types
- Free planYes
- Paid fromNone
- Feature attributionYes
- Facts checked28 Sep 2026
Where it wins
- Fast Tree SHAP support for XGBoost, LightGBM, CatBoost, scikit-learn, and PySpark
- Multiple explainer methods cover tree, linear, deep-learning, and arbitrary models
- Waterfall, force, text, and image plots help inspect individual predictions
Where it doesn't
- Python library rather than a hosted application
- Focused on feature attribution, without counterfactual explanations
- PyTorch support is preliminary
Our verdict on SHAP
SHAP is an open-source Python library that explains machine-learning outputs by assigning contributions to input features. It is aimed at data scientists and machine-learning practitioners who want to inspect individual predictions or patterns across datasets. Its scope spans tabular, text, and image inputs, making it relevant to teams working across different model types rather than those seeking a single-purpose explainer for tree models alone. SHAP can also decompose quantitative fairness disparities into feature contributions.
The breadth of explainers is its defining strength. Fast Tree SHAP algorithms support XGBoost, LightGBM, CatBoost, scikit-learn, and PySpark tree models. Other methods include linear, permutation, partition, sampling, deep-learning, kernel, gradient, and exact explainers. The library also covers transformers and arbitrary model functions; deep-learning support includes TensorFlow/Keras, while PyTorch support is preliminary. For communicating results, waterfall and force plots show individual prediction explanations, while scatter, beeswarm, bar, and heatmap plots show feature effects and importance. Text and image explanation plots extend the same attribution approach beyond tabular work.
SHAP is free and self-hosted, so it suits teams that want to work with an installable Python library rather than a hosted application. That format fits solo practitioners through enterprise teams, but requires working within a Python library workflow. Its focus is attribution: it does not provide counterfactual explanations or a full audit workflow. Teams whose central need is to trace feature contributions across model families may find its range useful; those prioritizing counterfactual analysis or a broader audit process should consider a different tool.
SHAP pricing
SHAP fact sheet
| Free plan | Yes |
|---|---|
| Paid from | None |
| Feature attribution | Yes |
| Counterfactual explanations | No |
| Fairness analysis | Yes |
| Audit reports | Not verified |
| Deployment options | Self_hosted |
| Supported model types | Tree ensembles (XGBoost, LightGBM, CatBoost, scikit-learn, PySpark); linear models; deep-learning models (TensorFlow/Keras, preliminary PyTorch); transformers; arbitrary model functions |
| Deployment | Self-hosted |
| Built for | Solo, Small business, Mid-market, Enterprise (editorial estimate) |
| Pricing | Free plan |
| Website | shap.readthedocs.io |
| Facts checked | 28 Sep 2026 |
Alternatives to SHAP
- 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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