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- # 6. Gradient Boosting
- # What it does: Builds one model at a time, each one trying to correct the previous one's mistakes.
- # Use Case: Customer churn prediction, click-through rate optimization.
- from sklearn.ensemble import GradientBoostingClassifier
- gb = GradientBoostingClassifier(n_estimators=100, random_state=42)
- gb.fit(X_train, y_train)
- y_pred = gb.predict(X_test)
- print("Gradient Boosting Confusion Matrix:\n", confusion_matrix(y_test, y_pred))
- print("\nGradient Boosting Classification Report:\n", classification_report(y_test, y_pred))
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