Compute SHAP feature importance and generate model explanation plots.
npx skills add https://github.com/sanand0/scientific-research --skill shap-sanand0
Please help me install this Agent Skill. Skill: shap Source: https://github.com/sanand0/scientific-research/tree/main/.claude/skills/shap Command: npx skills add https://github.com/sanand0/scientific-research --skill shap-sanand0
This Skill demystifies machine learning models by explaining their predictions and identifying key drivers, making complex AI understandable and trustworthy.
Use the shap skill to generate a beeswarm plot for the provided model explanations.
High-intent search queries and answers about installing and using this skill.
You can explain machine learning model predictions by computing SHAP values to quantify each feature's contribution. This skill generates visual plots like waterfall and beeswarm to show exactly how features drive individual outcomes and overall model behavior.
Yes, SHAP supports model interpretability for XGBoost and LightGBM. It calculates Shapley Additive exPlanations across various model architectures including tree-based models, deep learning frameworks, and linear models to identify feature importance.
The best way to debug machine learning models and analyze bias is by computing SHAP values to validate behavior and identify key drivers. This process pinpoints areas for improvement by revealing how specific features influence risk outcomes.
SHAP works with TensorFlow and PyTorch deep learning models to provide explainable AI. It computes feature importance and generates visual explanations for complex neural network predictions across different model architectures.
To generate a beeswarm plot for model explanations, use this skill to compute SHAP values and visualize feature importance distributions. It creates scatter plots that aggregate individual feature impacts across your entire dataset.
You should use SHAP values instead of basic feature importance because they provide both global and local model interpretability. They explain individual predictions using Shapley Additive exPlanations, revealing exactly how much each feature contributes to a specific outcome.
tassiovaleExplain machine learning model predictions using SHAP values and plots.
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jimmc414Generates SHAP values to explain feature importance and model predictions.
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