About ml-model-explainer
ml-model-explainer is a Python tool for interpreting machine-learning predictions, aimed at practitioners who need local and global explanations. It combines feature rankings, decision paths, and plots across several model types.
Key features
- 01SHAP explanations for individual predictions
- 02Global feature importance rankings
- 03Prediction decision paths
- 04Waterfall, force, and summary plots
- 05Support for tree, linear, and neural models
- 06Batch prediction explanations
Use cases
- 01Inspect why a single prediction was made
- 02Rank features across a model
- 03Explain predictions for a batch
- 04Export a waterfall plot from an explanation
Install
Copy the command and run it in a terminal.
$
npx skills add https:/After installing, type/ml-model-explainerin Claude Code to confirm it loaded.
Bundled filesscripts/ml_model_explainer.pyscripts/requirements.txt
Details
From the project READMEML 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
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