Shap

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Shap is an open-source game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using classic Shapley values.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is shap?

SHAP (SHapley Additive exPlanations) provides a game theoretic approach to interpret the outputs of various machine learning models. It connects optimal credit allocation with local explanations using classic Shapley values and their extensions. The tool supports high-speed exact algorithms for tree ensemble methods like XGBoost, LightGBM, and CatBoost, as well as specific support for natural language models from Hugging Face transformers. It also offers DeepExplainer for TensorFlow/Keras models and GradientExplainer for TensorFlow/Keras/PyTorch models, alongside a model-agnostic KernelExplainer for any function. SHAP helps users understand feature importance and interaction effects through various visualization plots.

Best used for

Ideal for data scientists who need to interpret complex machine learning model predictions, understand the contribution of individual features, and visualize model behavior. Especially valuable for researchers and practitioners working with tree ensembles, deep learning, or natural language processing models to ensure transparency and trust in AI systems.

Common actions

explain machine learning models

interpret model predictions

visualize feature importance

understand model behavior

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Capabilities

Key features

Explain ML model output
Shapley values for local explanations
High-speed tree ensemble algorithms
Natural language model support
Deep learning model explainers
Model-agnostic KernelExplainer
Interactive visualization plots

Target Audience

data scientistprofessor

Integrations

xgboostlightgbmcatboostscikit-learnpysparkhugging-face-transformerstensorflowkeras+ 1 more

Pricing & Plans

Open Source

Free

FAQs

What types of machine learning models can SHAP explain?

SHAP can explain the output of any machine learning model. It has specialized high-speed algorithms for tree ensemble methods (XGBoost, LightGBM, CatBoost, scikit-learn, pyspark), natural language models (Hugging Face transformers), and deep learning models (TensorFlow, Keras, PyTorch).

Does SHAP provide GPU acceleration for explanations?

Yes, SHAP supports GPU-accelerated Tree SHAP. To enable this, you need to install SHAP from source with the CUDA toolkit available and the SHAP_ENABLE_CUDA environment variable set during installation.

What are SHAP interaction values and how are they used?

SHAP interaction values are a generalization of SHAP values that reveal higher-order interactions between features. For tree models, fast exact computation of pairwise interactions is available, providing a matrix where main effects are on the diagonal and interaction effects are off-diagonal, uncovering hidden relationships.

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This tool also appears in

AI Agents & Automation › AI Frameworks & Infra Coding & Development › Open Source & Models Data & Analytics › Predictive Analytics

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