Probabilistic Churn Prediction with Ensemble Learning

Construct a sophisticated churn prediction model using an ensemble of machine learning algorithms (Random Forest, XGBoost, and Neural Networks) that goes beyond traditional binary classification. Implement a probabilistic framework that not just predicts churn likelihood, but provides granular risk segmentation with confidence intervals. Include advanced feature engineering techniques like interaction terms, time-decay weights for historical behavior, and external data source integration.