Group for Research in Decision Analysis

Random forests: Review and recent developments

Denis Larocque Professor, Department of Decision Sciences, HEC Montréal, Canada

Denis Larocque

Webinar link
Webinar ID: 984 0628 8966
Passcode: 860338

Random forests (Breiman, 2001), that are now part of the essential toolbox of any data scientist, are very powerful non-parametric statistical learning methods that can be used for classification, regression and many other problems including survival data. We will provide a historical review of random forests, discuss their basic properties, and present some of the many extensions to complex settings. Random forests are still a very active area of research and we will provide an overview of current topics and recent developments.