# Random forest

> An ensemble learning method for classification, regression and other tasks that works by creating a multitude of decision trees during training.

An [ensemble learning](https://wiki.g15e.com/pages/Ensemble%20model.txt) method for [classification](https://wiki.g15e.com/pages/Classification%20model.txt), [regression](https://wiki.g15e.com/pages/Regression%20model.txt) and other tasks that works by creating a multitude of <decision trees> during [training](https://wiki.g15e.com/pages/Training%20(machine%20learning.txt)).

Random forests correct for <decision trees>' habit of [overfitting](https://wiki.g15e.com/pages/Overfitting.txt) to their [training set](https://wiki.g15e.com/pages/Training%20data.txt).

## Publications

- [k-DT: A Multi-Tree Learning Method](https://wiki.g15e.com/pages/k-DT%20-%20A%20Multi-Tree%20Learning%20Method.txt): 여러개의 <Decision tree>를 이용하여 성능을 개선하는 방법을 제안한 첫 논문