Agent-Skills.md

Agent Skills: ML Model Explainer

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

UncategorizedID: dkyazzentwatwa/chatgpt-skills/ml-model-explainer

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dkyazzentwatwa/chatgpt-skills
dkyazzentwatwa
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ml-model-explainer/SKILL.md

Skill Metadata

Name
ml-model-explainer
Description
Explain ML model predictions using SHAP values, feature importance, and decision paths with visualizations.

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