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AI Evaluation & Observability

Shap

Shap is an open-source machine-learning explainability library that uses Shapley-value methods to assign feature contributions to model outputs and visualize those contributions for individual predictions and datasets.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Shap — OpenViST project profile in AI Evaluation & Observability.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Shap after or alongside model development when you need feature-level explanations, visual analysis of predictions, or a way to inspect how inputs influence outputs.

Who it is for

  • Machine-learning developers adding prediction explanations to Python workflows.
  • Data scientists investigating feature effects and model behavior.
  • Teams preparing model-debugging dashboards, notebooks or interpretability reports.

Documented uses

  • Explain why an individual model prediction moved from a baseline to its output.
  • Compare feature contributions across many predictions.
  • Inspect feature effects and interactions across a dataset.
  • Summarize global feature importance using SHAP values.
  • Add interpretability checks to an ML evaluation workflow.
Before you choose

Requirements & limits

The library is publicly available under the MIT license through PyPI and conda-forge. Cloud and self-hosted use are identified. GPU-accelerated Tree SHAP requires the CUDA toolkit and a source installation with SHAP_ENABLE_CUDA set.

  • Choose it when explanations need both local views, such as waterfall or force plots, and global views, such as beeswarm or bar plots.
  • Tree-model users can use the documented fast C++ Tree SHAP implementations for XGBoost, LightGBM, CatBoost, scikit-learn and PySpark tree models.
  • Natural-language workflows have documented support for supported Hugging Face transformers pipelines.
  • GPU acceleration has a separate source-installation and CUDA prerequisite.
  • Model-specific behavior and compatibility should be checked against the relevant workflow because the project material does not detail every model integration.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Shap for a model-explanation workflow

Set up Shap from PyPI or conda-forge so a machine-learning workflow can use its explanation functionality.

Before you start

  • A Python machine-learning environment.
  • Access to either PyPI or conda-forge.
  • For GPU-accelerated Tree SHAP, the CUDA toolkit and a source-installation workflow.

Steps

  1. Install Shap from PyPI with the documented package installation command, or install it from conda-forge.

  2. For GPU-accelerated Tree SHAP, install from source with the CUDA toolkit available and set SHAP_ENABLE_CUDA during installation.

  3. Use the installed library in a model-explanation workflow.

Expected outcome

Shap is installed through a documented package channel, with the documented source-installation path available for GPU-accelerated Tree SHAP.

Guide limitations

  • The supplied material does not provide a complete environment matrix or implementation details for every supported model.
  • Source-based guidance is not a compatibility or execution test.
Documentation 1 ↗Documentation 2 ↗
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗
Source record details
Source availability
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Sources checked
1 October 2026
Original website title
shap/shap · Official homepage title · retained in its source language
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