# Ensemble model

> In ML, ensemble model is a model that combines multiple models to achieve better performance than any single model could.

In [ML](https://wiki.g15e.com/pages/Machine%20learning.txt), ensemble model is a model that combines multiple models to achieve better performance than any single model could.

## Concepts

- **Bagging** creates diversity by generating random samples from the training observations and fitting the same model to each different sample - also known as *homogeneous parallel ensembles*.
- **Boosting** follows an iterative process by sequentially training each base model on the up-weighted errors of the previous base model, producing an additive model to reduce the final model errors.

## Instances

- [Random forest](https://wiki.g15e.com/pages/Random%20forest.txt) (an extension of bagging)
- [Gradient boosting](https://wiki.g15e.com/pages/Gradient%20boosting.txt) (boosting)