SHAP
A Python library that shows how much each input feature pushed a machine learning model's prediction up or dow
- Stars
- 25.8k
- Forks
- 3.8k
- Licence
- MIT
- Language
- Jupyter Notebook
- Last update
- 5 days ago
- Type
- Library
What it is
SHAP is a Python library for explaining the predictions of machine learning models. It uses Shapley values, a method from game theory, to assign each input feature a share of credit for a given prediction relative to a baseline average. People use it to understand why a model made a particular decision and to check which features matter overall.
- Explain a house price prediction from a gradient-boosted model with a waterfall plot
- Create a beeswarm plot to summarize feature importance across a dataset
- Visualize how a single feature, such as latitude, affects predicted output with a scatter plot
- Explain sentiment predictions from a Hugging Face transformers pipeline with text highlighting
Great for
- Data scientists who need to explain tree-based models such as XGBoost, LightGBM, or CatBoost
- Analysts who want to see which features drive predictions across a whole dataset
- People working with TensorFlow or Keras models who want image or deep-learning explanations
- Students learning model interpretability with the included example notebooks
Not ideal for
- People who only need a finished app and not a Python library, since SHAP requires writing code
- Users of models where the README gives no guidance on support, because only some model types have fast, specialized explainers; others may be slower or approximate
- Anyone needing a guarantee that explanations are causal; the README describes attributions, not causal effects
How to get it
- 1
Install with a package manager
You'll need: Python (version not stated in the provided README excerpt) · pip or conda
Install from PyPI
pip install shapInstall from conda-forge
conda install -c conda-forge shapInstall with GPU support (requires CUDA toolkit)
pip install shap[cuda] - 2
Run it
- Install a machine learning library such as XGBoost and train a model on a dataset
- Import shap and create an explainer with shap.Explainer(model)
- Compute values with explainer(X) and pass the result to a plot such as shap.plots.waterfall
- Open the included notebooks or the documentation at shap.readthedocs.io for more examples
- 3
Get the most out of it
- Use shap.plots.beeswarm on the full explanation to see which features matter most overall
- Use shap.plots.scatter with color=shap_values to let SHAP pick an interaction feature automatically
- Use GPUTreeExplainer for tree models if you have a CUDA-capable GPU and the cuda extra installed
- Pass a transformers pipeline directly to shap.Explainer to explain text model outputs
Downloads
Strengths
- Gives per-prediction explanations as well as dataset-wide summaries
- Provides fast exact algorithms for common tree ensemble models, according to the README
- Includes a range of plot types such as waterfall, force, beeswarm, and bar charts
Trade-offs
- Requires Python and familiarity with machine learning code; it is not a point-and-click tool
- Deep learning support is described as partly preliminary for PyTorch, and GPU tree explanations need a CUDA toolkit installed separately
- The README is truncated, so details on speed, limits, and some model types are not fully confirmed here
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| shap (this) | 25.8k | Library | intermediate | 5 days ago | Data scientists who need to explain tree-based models such as XGBoost, LightGBM, or CatBoost |
| awesome-production-machine-learning | 21.1k | Curated list | beginner | 5 days ago | ML engineers looking for tools to deploy or monitor models |
| awesome-datascience | 30.3k | Curated list | beginner | 6 days ago | Beginners who want a guided set of links to start learning data science |
| LightGBM | 18.9k | Library | intermediate | 4 days ago | Data scientists working with tabular data who want a fast gradient boosting tool |
| cheatsheets-ai | 15.4k | Learning resource | beginner | 7.1 years ago | Students who want a one-page summary of a library's main functions |
| albumentations | 15.3k | Library | beginner | 1.4 years ago | Machine learning engineers training image classification or segmentation models with limited data |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.