Ensemble Learning
A machine learning paradigm where multiple models are trained and combined to solve the same problem, improving overall performance.
Combines diverse base learners (trees, neural nets) via strategies like bagging, boosting, or stacking to reduce variance and bias. Ensembles require careful diversity management, calibration of combined outputs, and governance checks to ensure interpretability and resource efficiency. They often outperform single models but demand more compute and robust monitoring of each component.
A credit-risk department uses a stacked ensemble - combining logistic regression, random forest, and neural network - to score loan applications. The ensemble improves AUC by 5% over any single model. Governance mandates separate monitoring dashboards for each base model to quickly identify component failures.
“What used to take weeks of manual reviews and policy work is now structured and auditable in Enzai within minutes. It’s the first time AI governance has felt operational, not theoretical.”
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