Can I use SHAP for model debugging?

Yes, the skill provides specific workflows to identify misclassified samples, detect data leakage, and validate that feature relationships align with domain expectations.

Which machine learning models are compatible with this SHAP skill?

This skill supports a wide range of models including tree-based ensembles (XGBoost, LightGBM, Random Forest), deep learning frameworks (TensorFlow, PyTorch), linear models, and generic black-box models.

What is the difference between global and local explanations in SHAP?

Global explanations (like beeswarm or bar plots) show how features impact the model's overall behavior, while local explanations (like waterfall or force plots) explain the specific factors behind a single prediction.

How do I choose the correct SHAP explainer?

The skill guides you to use TreeExplainer for trees, DeepExplainer for neural networks, LinearExplainer for linear models, or KernelExplainer for any other model type to ensure optimal speed and accuracy.

SHAP Machine Learning Interpretability

Name: SHAP Machine Learning Interpretability
Author: Zehong-Wang

byZehong-Wang

0•

Data Science & ML

Explains machine learning model predictions and calculates feature importance using unified Shapley Additive exPlanations.

The SHAP skill provides a comprehensive toolkit for implementing explainable AI (XAI) within your machine learning workflows. It enables developers to compute exact feature attributions for any model type—including tree-based models, deep learning, and linear systems—using game theory-based Shapley values. Beyond raw computation, the skill guides users through generating sophisticated visualizations like beeswarm and waterfall plots, performing model debugging, identifying data leakage, and auditing models for algorithmic bias and fairness.

Key Features

01Automated selection of optimized explainers for Tree, Deep Learning, and Linear models.

02Comprehensive visualization suite for global feature importance and individual prediction breakdowns.

03Advanced workflows for identifying model bias and ensuring demographic fairness.

04Performance optimization strategies for large datasets and production-grade explanation services.

05Standardized patterns for model debugging and feature engineering insights.

060 GitHub stars

Use Cases

01Generating human-readable explanations for individual model predictions in regulated industries.

02Debugging model performance by identifying unexpected feature interactions or data leakage.

03Comparing feature importance across different model architectures to select the most interpretable version.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add zehong-wang/kosmos shap

For use in Claude.ai and ChatGPT