shap-model-explainability
CommunityUnlock ML model insights with SHAP.
Data & Analytics#data science#machine learning#model interpretability#feature importance#shap#explainable ai
Authorjaechang-hits
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill demystifies complex machine learning models by explaining their predictions and feature importance, making them transparent and trustworthy.
Core Features & Use Cases
- Model Interpretability: Understand which features drive predictions using Shapley values.
- Feature Importance: Quantify the impact of each feature on model outcomes, both globally and for individual predictions.
- Use Case: After training a customer churn prediction model, use this Skill to identify the top 3 factors (e.g., contract duration, monthly charges, customer service calls) that most influence a customer's likelihood to churn.
Quick Start
Explain the SHAP values for the test dataset using the trained XGBoost model and visualize the global feature importance with a beeswarm plot.
Dependency Matrix
Required Modules
shapmatplotlibxgboostlightgbmtensorflowtorch
Components
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: shap-model-explainability Download link: https://github.com/jaechang-hits/SciAgent-Skills/archive/main.zip#shap-model-explainability Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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