Explain machine learning model predictions using SHAP values and plots.
npx skills add https://github.com/felixboehm/biochem-allergy --skill shap-felixboehm
Please help me install this Agent Skill. Skill: shap Source: https://github.com/felixboehm/biochem-allergy/tree/main/.claude/skills/shap Command: npx skills add https://github.com/felixboehm/biochem-allergy --skill shap-felixboehm
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 model explanations.
High-intent search queries and answers about installing and using this skill.
Model interpretability is achieved by calculating SHAP values to quantify each feature's impact on predictions. This Skill generates visualizations like waterfall, beeswarm, and force plots to explain individual decisions and identify key drivers.
Feature importance for black-box models is calculated using SHapley Additive exPlanations to quantify the exact impact of each input variable. This approach supports tree-based, deep learning, and linear models.
Bias analysis and model debugging are supported by generating SHAP values to validate behavior and identify feature contributions. You can highlight the exact factors that contributed to a specific applicant's denial.
Beeswarm plots for model explanations are generated by processing your trained model and dataset with this Skill. It automatically calculates the SHAP values and renders the visualization to show feature importance distributions.
SHAP works with tree-based, deep learning, linear, and black-box models to provide explainable AI. It calculates Shapley Additive Explanations across these frameworks to ensure consistent model interpretability.
SHAP values provide explainable AI by offering a unified measure of feature importance grounded in game theory. Unlike basic methods, it facilitates both individual prediction explanation and global model debugging through detailed visual plots.
ManfronEnricoCompute SHAP values and visualizations for tree, deep, and linear models.
youyinnnExplain machine learning model predictions using SHAP values and plots.
interflownexStandardize machine learning workflows with strict data preprocessing and validation protocols.
hxk622Compute SHAP values and generate feature importance plots for machine learning models.
