Generate SHAP values and beeswarm plots to explain model predictions.
npx skills add https://github.com/Rowtion/Bioclaw --skill shap-rowtion
Please help me install this Agent Skill. Skill: shap Source: https://github.com/Rowtion/Bioclaw/tree/main/scientific-skills/shap Command: npx skills add https://github.com/Rowtion/Bioclaw --skill shap-rowtion
This Skill demystifies machine learning models by explaining their predictions, identifying key drivers, and revealing how features influence outcomes, making AI transparent and trustworthy.
Use the shap skill to generate a beeswarm plot for the provided SHAP values and test data.
High-intent search queries and answers about installing and using this skill.
You can explain feature importance in your XGBoost model by calculating SHAP values. This Skill uses TreeExplainer to quantify the impact of each feature on model outputs, allowing you to identify key drivers behind predictions.
Yes, you can use SHAP to explain deep learning models built with TensorFlow. This Skill utilizes DeepExplainer to break down how individual features influence predictions in complex neural networks, making AI transparent.
The best way to visualize why a specific prediction was made is by generating SHAP plots like beeswarm charts. This Skill creates visual prediction breakdowns showing exactly how each feature contributes to a specific model output.
Yes, this SHAP approach works with scikit-learn and LightGBM models. It supports tree-based, linear, and black-box models through specialized explainers like TreeExplainer and KernelExplainer for comprehensive interpretability.
You debug machine learning model fairness using SHAP values by analyzing how individual features influence outcomes across different groups. This Skill facilitates fairness analysis by revealing feature impacts and demystifying model predictions.
You should use KernelExplainer for model interpretability when working with black-box models that lack specialized explainers. It provides SHapley Additive exPlanations for any model type, ensuring AI transparency across unsupported architectures.
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