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Shap — shap

SHAP is a Python library that applies game-theoretic Shapley values to explain machine-learning outputs by attributing contributions to input features.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Shap — shap — OpenViST project profile in AI Evaluation & Observability.
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Understand the project

Practical overview

When it fits

Use SHAP when you have a model and want local explanations for its predictions or aggregate views of feature effects across a dataset.

Who it is for

  • Machine-learning practitioners who need to interpret model predictions.
  • Analysts comparing feature effects across examples or datasets.

Documented uses

  • Explain which features push an individual prediction higher or lower.
  • Visualize feature contributions across multiple model predictions.
  • Inspect how a feature relates to model output and possible interactions.
  • Summarize feature importance using attribution values.
Before you choose

Requirements & limits

The library is open source under the MIT license and can be installed from PyPI or conda-forge. GPU-accelerated Tree SHAP requires installation from source and the CUDA toolkit.

  • The README identifies fast C++ implementations for XGBoost, LightGBM, CatBoost, scikit-learn and pyspark tree models.
  • The project describes specific support for natural-language models through Hugging Face Transformers pipelines.
  • GPU acceleration is documented for Tree SHAP and has a CUDA toolkit prerequisite.

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 from PyPI or conda-forge

Set up the SHAP Python library using one of its documented package channels.

Before you start

  • A Python environment with the selected package manager available.

Steps

  1. Choose either PyPI or conda-forge as the installation channel.

  2. For PyPI, run the command below; alternatively, for conda-forge, run conda install -c conda-forge shap.

    Documented command
    pip install shap

Expected outcome

SHAP is installed through the selected package channel.

Guide limitations

  • The source does not give additional environment setup or troubleshooting steps.
  • GPU-accelerated Tree SHAP has separate requirements; see the repository README.
Documentation 1 ↗
Source-based practical guide

Explain a tree-ensemble model prediction with SHAP

Generate SHAP values for an XGBoost model and visualize one prediction's feature contributions.

Before you start

  • SHAP installed.
  • XGBoost available in the Python environment.
  • A trained or trainable XGBoost regression setup and input data; the README example uses SHAP's California dataset.

Steps

  1. Import xgboost and shap.

  2. Load the California dataset with X, y = shap.datasets.california() and fit model = xgboost.XGBRegressor().fit(X, y).

  3. Create an explainer and calculate values with explainer = shap.Explainer(model) and shap_values = explainer(X).

  4. Visualize the first prediction using shap.plots.waterfall(shap_values[0]).

Expected outcome

A waterfall visualization displays feature contributions for the first prediction, showing how they move the model output from its base value.

Guide limitations

  • The example demonstrates XGBoost; fast C++ tree implementations are also identified for LightGBM, CatBoost, scikit-learn, and pyspark models.
  • The base value in the example is the average model output over the training dataset passed to the explainer.
Documentation 1 ↗
Evidence and transparency

Sources & review

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