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