# Ensemble (Edge AI Glossary)

## metadata
- term: Ensemble
- slug: ensemble
- letter: E
- route: /resources/glossary/ensemble
- canonical: https://klyff.com/resources/glossary/ensemble

## Definition & Context
In Edge AI, an ensemble is a group of multiple AI models working together to improve prediction accuracy and decision-making. Also called model aggregation, it leverages diverse...

## Details
## Ensemble

#### What is Ensemble?

In Edge AI, an ensemble is a group of multiple AI models working together to improve prediction accuracy and decision-making. Also called model aggregation, it leverages diverse model outputs to produce more reliable results at the edge.

#### Why Is It Used?

Ensembles are used to reduce errors and improve robustness in AI predictions, especially in resource-constrained Edge AI devices where reliability is critical.

#### How Is It Used?

Data from edge sensors is processed by multiple AI models simultaneously. Their outputs are combined using methods like averaging, voting, or weighted scoring to generate a final, more accurate prediction.

#### Types of Ensemble

 
- **Bagging:** Combines multiple models trained on different data subsets.
- **Boosting:** Sequentially trains models, focusing on prior errors.
- **Stacking:** Uses outputs of several models as input for a final model. 

#### Benefits of Ensemble

 
- Enhanced accuracy and reliability.
- Greater robustness against sensor noise or edge environment variability.
- Reduced risk of overfitting with diverse model perspectives.
- Optimized performance for real-time Edge AI applications.