Compute SHAP feature attributions and generate explanation plots for machine learning models.
npx skills add https://github.com/Weiwei-Mao/hydrology-skills --skill shap-weiwei-mao
Please help me install this Agent Skill. Skill: shap Source: https://github.com/Weiwei-Mao/hydrology-skills/tree/main/hydrology-skills/shap Command: npx skills add https://github.com/Weiwei-Mao/hydrology-skills --skill shap-weiwei-mao
This Skill provides deep insights into how machine learning models make predictions, helping users understand feature importance, debug models, and ensure fairness.
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 and understand feature importance, you compute SHAP values to quantify the impact of each feature on model outcomes, generating plots like waterfall or beeswarm to visualize the attributions.
Yes, you can use SHAP values to debug model bias and analyze fairness for black-box models by computing feature attributions to identify which features drive specific predictions and validate model behavior.
Model interpretability with SHAP works with tree-based models, deep learning, linear models, and black-box models, computing feature attributions to provide deep insights into how complex models make predictions.
The best way to visualize feature attributions for explainable AI is generating various plots such as waterfall, beeswarm, bar, scatter, force, and heatmap to understand complex model behavior and feature impact.
To generate a beeswarm plot for model explanations, you use the SHAP skill after training your machine learning model to compute feature attributions and visualize the distribution of feature importance across predictions.
To understand why your loan approval model denies a specific applicant and what factors contributed, you compute SHAP values to highlight the key features that drove the model's decision for that individual prediction.
gracefullightExplain machine learning model predictions using SHAP values and plots.
Ritabrata-ChakrabortyComputes SHAP values to interpret ML model predictions and feature importance.
sanand0Compute SHAP feature importance and generate model explanation plots.
tondevrelAnalyze scikit-learn model feature importance with SHAP, LIME, and partial dependence plots.
