AgentSkillsCN

ml-model-explainer

使用 SHAP 值、特征重要性和决策路径解释机器学习模型预测结果并提供可视化。

SKILL.md
--- frontmatter
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

python
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

bash
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