Compute SHAP values and generate feature attribution plots for machine learning models.
npx skills add https://github.com/dralkh/seerai --skill shap-dralkh
Please help me install this Agent Skill. Skill: shap Source: https://github.com/dralkh/seerai/tree/main/skills/shap Command: npx skills add https://github.com/dralkh/seerai --skill shap-dralkh
It turns opaque model predictions into clear feature-level explanations so you can understand why a model made a decision, compare models, and validate behavior with confidence.
Ask for SHAP-based explanation guidance for your model and specify the model type, dataset, and the plot or workflow you want to produce.
High-intent search queries and answers about installing and using this skill.
SHAP values explain machine learning predictions by computing feature attributions for individual instances. You select the correct explainer for your model type, calculate local and global attributions from background data, and generate visual plots to understand feature influence.
Yes, SHAP feature attribution applies to tree-based models, neural networks, linear models, and black-box estimators. It requires selecting the model-specific explainer algorithm to accurately compute attributions based on the estimator architecture.
To visualize model interpretability for a single prediction, generate SHAP waterfall or force plots. These visualizations break down local feature attributions to show exactly how each feature value pushed the model's prediction higher or lower.
Perform fairness analysis on a classification model by computing SHAP values to check whether protected attributes or proxy variables are driving the outcome. This identifies if specific features disproportionately influence predictions across different demographic groups.
For global feature importance interpretation, generate SHAP beeswarm, bar, scatter, heatmap, and violin plots. These visualizations aggregate local feature attributions across the dataset to display overall feature importance and distribution patterns.
Yes, SHAP model interpretation for black-box estimators requires background data to compute feature attributions. The background dataset serves as a reference distribution to approximate missing features and calculate accurate SHAP values.
jasrajtulsiExplain model predictions by attributing outcomes to input features with SHAP values.
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CRAG666Compute SHAP values and generate global and local explanation plots.
xiechyCompute SHAP values to attribute predictions to features across model types.
