Introduction to classification and regression trees, random forests and model-based recursive partitioning in R

Novice and advanced R users from all professional groups who have basic knowledge in statistics.

• CHF 600.- für Angehörige der UZH/ETH und assoziierter Institute
• CHF 800.- für Alumni der UZH/ETH, Angehörige anderer Universtitäten, Forschungseinrichtungen und Ämter des Bundes oder der Kantone, non-profit Organisationen
• CHF 1200.- für Firmen

In this course, a range of tree-based methods like classification and regression trees, bagging, random forests and model-based recursive partitioning will be introduced. Tree-based methods provide state-of-the-art data science tools for decision-making and prediction. In contrast to many other statistical methods, trees are easy to interpret and apply. In addition to methods for individual trees, the course will also cover ensemble methods like random forests, that combine hundreds of individual trees and score among the best-performing prediction methods in many machine learning competitions. Lectures focusing on theory and data-analytic examples will be alternated by practical sessions in which we will apply tree-based methods to real-world datasets.

The participants should be familiar with basic statistical concepts and techniques (such as linear regression). Prior experience with R is helpful but not mandatory.

For all Zurich R Courses participants should bring their own laptops to the course and will be informed by email in advance which packages they need to install.

Mai 15-16, 2017