Explain machine learning model predictions using SHAP and feature importance.
Features
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SHAP Values: Explain individual predictions
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Feature Importance: Global feature rankings
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Decision Paths: Trace prediction logic
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Visualizations: Waterfall, force plots, summary plots
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Multiple Models: Support for tree-based, linear, neural networks
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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
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shap>=0.42.0
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scikit-learn>=1.3.0
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pandas>=2.0.0
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numpy>=1.24.0
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matplotlib>=3.7.0