AI & Analytics
Classification trees and ensemble techniques with an application in R
Aspiring Data Scientists with a wish to deepdive on the most important technologies.
Formal training. In the extended version: practice work in R included.
Knowledge of programming basics (preferably, but not necessarily, in R)
In case of the extended trainig, bring a laptop with RStudio installed
3 hours (excl. coding exercises), 4-5 hours (incl. coding exercises)
Prefered group size:
8 participants per trainer (scalable)
This course introduces Classification trees and Ensemble Techniques. Classification trees are popular because they provide intuitive results - which helps both fine-tuning and end-user acceptance. This course explains how they are actually made. Next, ensemble techniques are introduced. these techniques are used in almost all highly optimized data science solutions. How are sub-parts optimally set to work together? There will be the opportunity to try out both methods for ourselves - without the use of your computer! In addition, useful R-examples are provided afterwards, including C5, Random Forests, bagging and stacking.
Upon completion of this training, participants will have a thorough understanding of under-the-hood working of Decision Tree methods and ensemble techniques.