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shap

davila7/claude-code-templates

SHAP is a unified approach to explain machine learning model outputs using Shapley values from cooperative game theory. This skill provides comprehensive guidance for:

Installs142
Install command
npx skills add https://github.com/davila7/claude-code-templates --skill shap
About this skill
SHAP is a unified approach to explain machine learning model outputs using Shapley values from cooperative game theory. This skill provides comprehensive guidance for: SHAP works with all model types: tree-based models (XGBoost, LightGBM, CatBoost, Random Forest), deep learning models (TensorFlow, PyTorch, Keras), linear models, and black-box models. Trigger this skill when users ask about : Decision Tree : Tree-based model? (XGBoost, LightGBM, CatBoost, Random Forest, Gradient Boosting) Deep neural network? (TensorFlow, PyTorch, Keras, CNNs, RNNs, Transformers) Linear model? (Linear/Logistic Regression, GLMs) Any other model? (SVMs, custom functions, black-box models) Unsure? See references/explainers.md for detailed information on all explainer types.

Source description provided by the upstream skill listing. Community reviews and install context appear in the sections below.

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FAQ
What does shap do?

SHAP is a unified approach to explain machine learning model outputs using Shapley values from cooperative game theory. This skill provides comprehensive guidance for:

Is shap good?

shap does not have approved reviews yet, so SkillJury cannot publish a community verdict.

What agent does shap work with?

shap currently lists compatibility with codex, gemini-cli, opencode, cursor, claude-code, antigravity.

What are alternatives to shap?

Skills in the same category include telegram-bot-builder, flutter-app-size, sharp-edges, iterative-retrieval.

How do I install shap?

npx skills add https://github.com/davila7/claude-code-templates --skill shap

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