### Navigation

- index
- next |
- previous |
- OpenCV 2.4.10.0 documentation »
- OpenCV API Reference »
- ml. Machine Learning »

# Extremely randomized trees

Extremely randomized trees have been introduced by Pierre Geurts, Damien Ernst and Louis Wehenkel in the article “Extremely randomized trees”, 2006 [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.65.7485&rep=rep1&type=pdf]. The algorithm of growing Extremely randomized trees is similar to Random Trees (Random Forest), but there are two differences:

1. Extremely randomized trees don’t apply the bagging procedure to construct a set of the training samples for each tree. The same input training set is used to train all trees.
2. Extremely randomized trees pick a node split very extremely (both a variable index and variable splitting value are chosen randomly), whereas Random Forest finds the best split (optimal one by variable index and variable splitting value) among random subset of variables.

## CvERTrees

class `CvERTrees` : public `CvRTrees`

  The class implements the Extremely randomized trees algorithm. `CvERTrees` is inherited from `CvRTrees` and has the same interface, so see description of `CvRTrees` class to get details. To set the training parameters of Extremely randomized trees the same class `CvRTParams` is used.

## Help and Feedback

You did not find what you were looking for?

- Ask a question on the Q&A forum.
- If you think something is missing or wrong in the documentation, please file a bug report.

### Navigation

- index
- next |
- previous |
- OpenCV 2.4.10.0 documentation »
- OpenCV API Reference »
- ml. Machine Learning »