How do I choose the right SHAP explainer for my model?

The skill provides a decision guide: use TreeExplainer for trees, DeepExplainer for neural networks, LinearExplainer for linear models, and KernelExplainer as a model-agnostic fallback when others aren't applicable.

What model types does this SHAP skill support?

It supports a wide range of models including tree-based models like XGBoost and LightGBM, deep learning frameworks like PyTorch and TensorFlow, linear models, and any black-box model via KernelExplainer.

Does this skill help with SHAP performance issues?

Absolutely. It provides strategies for optimization such as background data sampling, batch processing, and selecting faster explainer variants to handle large-scale datasets.

Can this skill help with identifying model bias?

Yes, it includes specialized workflows for fairness analysis, allowing you to compare feature importance across different demographic cohorts and identify proxy features that may be causing bias.

Model Interpretability with SHAP

Name: Model Interpretability with SHAP
Author: ovachiever

byovachiever

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データサイエンスとML

Provides comprehensive guidance and implementation patterns for explaining machine learning model predictions using SHAP values.

概要

This skill integrates the SHAP (SHapley Additive exPlanations) framework into the Claude Code environment, enabling developers and data scientists to move beyond 'black-box' models. It offers structured workflows for computing feature importance, generating sophisticated visualizations like beeswarm and waterfall plots, and conducting deep-dive analyses into model bias and fairness. Whether working with tree-based models, deep learning, or linear regressions, this skill provides the specific code patterns and optimization strategies needed to implement explainable AI effectively in both research and production settings.

主な機能

Fairness and bias analysis across protected demographic groups
Performance optimization strategies for large-scale datasets and production
Structured workflows for model debugging and data leakage detection
Selection logic for the optimal explainer based on model architecture
Automated patterns for global and local feature importance visualizations
15 GitHub stars

ユースケース

Debugging unexpected model predictions to identify data quality issues or hidden leakage.
Generating regulatory-compliant documentation for explainable AI in financial or healthcare sectors.
Comparing feature importance across multiple model architectures to select the most robust candidate.

概要

主な機能

Fairness and bias analysis across protected demographic groups
Performance optimization strategies for large-scale datasets and production
Structured workflows for model debugging and data leakage detection
Selection logic for the optimal explainer based on model architecture
Automated patterns for global and local feature importance visualizations
15 GitHub stars

ユースケース

Debugging unexpected model predictions to identify data quality issues or hidden leakage.
Generating regulatory-compliant documentation for explainable AI in financial or healthcare sectors.
Comparing feature importance across multiple model architectures to select the most robust candidate.