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explaining-machine-learning-models

Explain trained machine learning models through feature attribution, local explanations, and behavior summaries. Use as an explicit/manual helper once a model already exists, not for training ownership, leakage auditing, or general ML strategy selection.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

foryourhealth111-pixel/Vibe-Skills
설치 방법 보기

포함된 파일(10)

  • SKILL.md1.5 KB
  • assets/example_explanation.json3.5 KB
  • assets/explanation_template.html3.8 KB
  • assets/README.md362 B
  • assets/visualization_styles.css2.5 KB
  • references/README.md551 B
  • scripts/data_preprocessing.py2.8 KB
  • scripts/explain_model.py2.9 KB
  • scripts/feature_importance.py2.8 KB
  • scripts/README.md407 B

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Model Explainability Tool

Positioning

Treat this skill as an explicit/manual helper for interpretability work.

When to Use

Use this skill when:

  • Understand why a machine learning model made a specific prediction.
  • Identify the most important features influencing a model's output.
  • Debug model performance issues by identifying unexpected feature interactions.
  • Communicate model insights to non-technical stakeholders.
  • Ensure fairness and transparency in model predictions.

Not For / Boundaries

  • Model training and hyperparameter search: use scikit-learn
  • Benchmark comparison and threshold selection: use evaluating-machine-learning-models
  • Leakage or prediction-time audits: use ml-data-leakage-guard

Typical Outputs

  • Feature importance or attribution summaries
  • Local explanation workflow for a concrete prediction
  • Notes on caveats, instability, or misleading explanations

Related Skills

  • shap for SHAP-specific workflows
  • evaluating-machine-learning-models when the question is whether the model is good enough

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