# Ml Model Explainer

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

- Skill: `majiayu000/ml-model-explainer` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add majiayu000/ml-model-explainer`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/ml-model-explainer/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/ml-model-explainer

---


# 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