Section outline

  • Lecture: Regression Trees and Random Forests

    Motivation for, and underpinnings of, the classification and regression tree paradigm will be detailed along with illustrative applications.  Shortcomings of the technique will be identified and the means whereby these can be redressed by random forests described.  Properties of random forests will be highlighted and examples presented.

    Faculty:  Mark Segal


    Please watch 2019 recorded lectures

    • Session Slides:

    • MachineLearningInR lectureX Trees Forests File

    • Session Audio/Video Recording (Access restricted to registered students):

    • Watch 2019 Recording URL
      Not available unless: You belong to a group in Registered Students Only

    • Assignment Due Date: Monday, May 11th.

    • Biostat216HW2v1 File
    • Biostat216HW2v1 File
    • Homework Assignment 2

    • Assignment Answer Key (Access restricted to registered students):

    • Biostat216HW2v1solutions File
    • Biostat216HW2v1solutions File