Ensemble Learning

Deep Ensemble refers to a machine learning technique that combines multiple models to improve prediction accuracy and robustness.

Ensemble diversity refers to the variety of models in an ensemble learning method, impacting its overall performance and robustness.

Majority voting is a decision-making process where the option with the most votes wins.

A Snapshot Ensemble combines multiple models trained at different times to improve prediction accuracy.

Stacking is a machine learning ensemble technique that combines multiple models to improve prediction accuracy.