[IP3-4-7-1] Random forest (RF)

The RF classifier is an ensemble classifier that uses a set of Classification and Regression Trees (CARTs) to make a prediction The trees are created by drawing a subset of training samples through replacement (a bagging approach).

Discussion

External resources

• Breiman, L. (2001). Random Forest. Machine Learning, 45 , Mariana Belgiu, Lucian Drăguţ, Random forest in remote sensing: A review of applications and future directions, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 114, 2016, Pages 24-31, (http://www.sciencedirect.com/science/article/pii/S0924271616000265)

Skills

• 1181 - Explain the sensitivity of the Random Forests classifier to the number of trees and the number of variables used to split the tree nodes

  Explain the sensitivity of the Random Forests classifier to the number of trees and the number of variables used to split the tree nodes

Status

Outgoing relations

• [IP3-4-7-1] Random forest (RF) is subconcept of [GC3-12] AI algorithms
• [IP3-4-7-1] Random forest (RF) is subconcept of [IP3-4-7] Machine learning

Incoming relations

• [IP3-4-5] Decision trees is prerequisite of [IP3-4-7-1] Random forest (RF)

Contributors

• Florian Albrecht
  https://www.researchgate.net/profile/Florian_Albrecht
• Mariana Belgiu
  https://research.utwente.nl/en/persons/mariana-belgiu
• Stefan Lang
  https://www.uni-salzburg.at/index.php?id=32483&L=1