Compute SHAP feature attributions and generate explanation plots for machine learning models.
npx skills add https://github.com/Yezez9/Research-Agent --skill shap-yezez9
Please help me install this Agent Skill. Skill: shap Source: https://github.com/Yezez9/Research-Agent/tree/main/scientific-skills/shap Command: npx skills add https://github.com/Yezez9/Research-Agent --skill shap-yezez9
This Skill demystifies machine learning models by explaining their predictions, identifying key drivers, and revealing how features influence outcomes.
Use the shap skill to generate a beeswarm plot for the model explanations.
High-intent search queries and answers about installing and using this skill.
To explain machine learning model predictions, you compute SHAP values to quantify feature attributions and identify key drivers. This approach generates visual plots like waterfalls and beeswarms to reveal how features influence individual predictions.
Yes, SHAP values work with XGBoost, LightGBM, TensorFlow, and PyTorch models. The Skill leverages these dependencies to calculate feature attributions and provide model interpretability across these different machine learning frameworks.
The best way to debug machine learning models for bias and data leakage is to analyze SHAP values for unexpected feature attributions. Identifying disproportionately influential features reveals hidden biases and pinpoints data leakage in your workflows.
To generate a beeswarm plot for model interpretation, the Skill calculates SHAP values and uses matplotlib for visualizations. This plot visualizes the distribution of feature impacts across your entire dataset to explain complex model behavior.
To understand why your loan approval model denies specific applicants, SHAP values provide prediction explanations by highlighting the features that contributed most to the decision. This quantifies individual feature attributions for transparent analysis.
tassiovaleExplain machine learning model predictions using SHAP values and plots.
hxk622Compute SHAP values and generate feature importance plots for machine learning models.
dralkhCompute SHAP values and generate feature attribution plots for machine learning models.
jimmc414Generates SHAP values to explain feature importance and model predictions.
