How to Develop an Extra Trees Ensemble with Python

Last Updated on August 17, 2020

Extra Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees.

It is related to the widely used random forest algorithm. It can often achieve as-good or better performance than the random forest algorithm, although it uses a simpler algorithm to construct the decision trees used as members of the ensemble.

It is also easy to use given that it has few key hyperparameters and sensible heuristics for configuring these hyperparameters.

In this tutorial, you will discover how to develop Extra Trees ensembles for classification and regression.

After completing this tutorial, you will know:

• Extra Trees ensemble is an ensemble of decision trees and is related to bagging and random forest.
• How to use the Extra Trees ensemble for classification and regression with scikit-learn.
• How to explore the effect of Extra Trees model hyperparameters on model performance.

Let’s get started.