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Claude-skill-registry-data ml-model-explainer

Explain ML model predictions using SHAP values, feature importance, and decision paths with visualizations.

install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry-data
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry-data "$T" && mkdir -p ~/.claude/skills && cp -r "$T/data/ml-model-explainer" ~/.claude/skills/majiayu000-claude-skill-registry-data-ml-model-explainer && rm -rf "$T"
manifest: data/ml-model-explainer/SKILL.md
source content

ML Model Explainer

Explain machine learning model predictions using SHAP and feature importance.

Features

  • SHAP Values: Explain individual predictions
  • Feature Importance: Global feature rankings
  • Decision Paths: Trace prediction logic
  • Visualizations: Waterfall, force plots, summary plots
  • Multiple Models: Support for tree-based, linear, neural networks
  • Batch Explanations: Explain multiple predictions

Quick Start

from ml_model_explainer import MLModelExplainer

explainer = MLModelExplainer()
explainer.load_model(model, X_train)

# Explain single prediction
explanation = explainer.explain(X_test[0])
explainer.plot_waterfall('explanation.png')

# Feature importance
importance = explainer.feature_importance()

CLI Usage

python ml_model_explainer.py --model model.pkl --data test.csv --output explanations/

Dependencies

  • shap>=0.42.0
  • scikit-learn>=1.3.0
  • pandas>=2.0.0
  • numpy>=1.24.0
  • matplotlib>=3.7.0