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pythonintermediate

SHAP Model Explainability in Python

Explain ML model predictions globally and locally using SHAP values with tree-based models.

python
import shap
import numpy as np
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.datasets import load_breast_cancer

X, y = load_breast_cancer(return_X_y=True, as_frame=True)
model = GradientBoostingClassifier(n_estimators=100, random_state=42)
model.fit(X, y)

explainer    = shap.TreeExplainer(model)
shap_values  = explainer.shap_values(X)

# Global feature importance
importance = np.abs(shap_values).mean(axis=0)
top5_idx   = np.argsort(importance)[::-1][:5]
print('Top 5 features:')
for idx in top5_idx:
    print(f'  {X.columns[idx]}: {importance[idx]:.4f}')

# Local explanation for first sample
print('\nFirst sample SHAP values:')
for col, val in zip(X.columns, shap_values[0]):
    if abs(val) > 0.01:
        print(f'  {col}: {val:+.4f}')

Use Cases

  • model interpretability
  • compliance
  • debugging ML models

Tags

#shap#explainability#xai#sklearn

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