Compute SHAP feature attributions and generate explanation plots for model predictions.
npx skills add https://github.com/olavocarvalho/data-agents --skill shap-olavocarvalho
Please help me install this Agent Skill. Skill: shap Source: https://github.com/olavocarvalho/data-agents/tree/main/skills/shap Command: npx skills add https://github.com/olavocarvalho/data-agents --skill shap-olavocarvalho
This Skill demystifies complex machine learning models by explaining their predictions, making them transparent and trustworthy.
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.
You explain machine learning model predictions using SHAP values by computing feature attributions that quantify each feature's contribution to a specific prediction, demystifying black-box model behaviors. The Skill generates visualizations like waterfall and beeswarm plots for transparency.
Yes, you can use SHAP to interpret deep learning models built with TensorFlow and PyTorch. The Skill supports computing feature attributions and generating explanation plots for deep learning models, alongside tree-based models like XGBoost and LightGBM.
The best way to identify feature importance in a credit risk model is by computing SHAP values to highlight key factors like credit score or debt-to-income ratio. This Skill generates global and local explanations to reveal which features most influence model outcomes.
Yes, SHAP supports generating beeswarm and scatter plots for model interpretability. The Skill produces diverse visualizations including waterfall and scatter plots to visually explain both global feature importance and local prediction attributions.
You should use SHAP values for model debugging and bias detection when you need to identify unexpected behaviors or biases in black-box predictions. The Skill provides feature attributions that pinpoint exactly why specific predictions are made, enabling trustworthy AI ethics checks.
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