Introduction to Machine Learning - Tree-based Models

Level: Intermediate, some experience required

Keywords: R, Machine Learning, Tree-based Models, Analytics

Note: Please see Prerequisite Section Below

View Session Slides - Opens new Tab

Download Session Files - Downloads .zip file

Session Summary:

Tree-based machine learning models, have provided the foundation for some of the most advanced, accessible and accurate machine learning techniques used in data science. During this session we will explore a theoretical and practical overview of decision trees as one example of tree-based methods and introduce how to interpret and evaluate these models.

Please note: Due to time constraints, this session will not cover Decision Tree Pruning, rather this will be covered in the next session Introduction to Machine Learning - Tree-based Models 2.

Session Objectives:

  • Provide a theoretical overview of Tree-Based Machine Learning Techniques
  • Provide a technical and practical overview of Decision Trees for classification and regression problems.
  • Understand how to interpret these models and evaluate them.

Transferable Skills:

  • Understand how to design, develop, and interpret Tree-based Machine Learning Models in R.
  • Using the packages tidyverse, caret & rpart

Prerequisite Knowledge:

Basic programming skills in R required, awareness and basic knowledge of machine learning techniques, applications and types useful but not essential.

Prerequisite Content:

Access to R & Rstudio (R’s Graphical User Interface, or RStudio Cloud (Free Online)), Provided ZIP File .zip.