{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence"}],"enrichment":{"capability":"SHAP computes Shapley values to explain individual predictions and feature importance across any machine learning model, using game-theoretic attribution to show how each feature contributes to model output.","skillfed_tags":["model-explainability","interpretability","feature-attribution"],"use_cases":["Explain individual predictions in production models to stakeholders or for regulatory compliance","Debug model behavior by identifying which features drive incorrect or unexpected predictions","Compare feature importance across different model types to validate model selection","Analyze transformer and NLP model outputs using coalitional Shapley rules for text-based predictions","Visualize deep learning model decisions via DeepExplainer for image or tensor-based models"],"what_it_does":"SHAP is a model-agnostic explainability library that uses Shapley values from game theory to attribute each feature's contribution to individual model predictions. It works across tree ensembles, transformers, deep learning models, and other architectures, providing both local explanations (why a single prediction was made) and global insights (which features matter most overall).\n\nThe library provides multiple visualization methods\u2014waterfall plots, force plots, dependence scatter plots, beeswarm plots, and bar charts\u2014to help interpret model behavior. It includes specialized fast algorithms for tree models and approximation methods for deep learning via DeepExplainer and GradientExplainer. Installation brings 10 runtime dependencies including numpy, scipy, scikit-learn, pandas, tqdm, packaging, slicer, numba, llvmlite, and cloudpickle; GPU support is optional but requires CUDA toolkit and environment setup.","worth_installing":"Yes. SHAP is actively maintained, widely used, has no known vulnerabilities, and uses permissive MIT licensing. Medium install friction is offset by comprehensive pre-built wheels and strong ecosystem support. Install if you need model explainability across any ML framework; skip only if your use case requires minimal dependencies or Python versions below 3.12."},"id":"shap","links":{"html":"https://skillfed.io/packages/shap","md":"https://skillfed.io/packages/shap.md","pypi":"https://pypi.org/project/shap/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-28","license_spdx":null,"license_treatment":"permissive","name":"shap","python_support":"supports_current","summary":"A unified approach to explain the output of any machine learning model."},"popularity":{"monthly_downloads":19814213,"position":1054,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.52.0"}