Ensemble Methods at Scale

Overview

This work demonstrates how to build highly accurate propensity models for credit card marketing campaigns using stacked ensemble methods. The approach handles millions of customer records while maintaining model interpretability and performance.

Key Results

• • Developed stacked ensemble models combining multiple base learners
• • Achieved superior lift over single-model approaches
• • Implemented efficient training pipelines for large-scale data
• • Maintained model interpretability for regulatory compliance

Technical Approach

The ensemble architecture combines gradient boosting, random forests, and logistic regression as base models, with a meta-learner that optimally weights their predictions. This multi-layer approach captures different aspects of customer behavior.

Technologies Used