Introduction to Classification Trees and Random Forests in R
Resources created by JR
This GitHub repository! contains all the material discussed in the workshop and will help you follow the recording. This includes a short presentation on using decision trees to describe rules for classifying data, and how multiple, randomized trees can get us to more accurate classifications. Then two R-markdown document are available to guide you through the code needed to fit classification trees and Random Forests using popular R packages.
Additional Resources
- Davis David: Random Forest Classifier Tutorial - How to Use Tree-Based Algorithms for Machine Learning
- Evan Muzzall and Chris Kennedy: Introduction to Machine Learning in R
- Github link: Machine Learning in R
- Github link: Machine Learning with Tidymodels
- Dave Tang: Building a classification tree in R
- Zach @ Statology: How to Fit Classification and Regression Trees in R
- Ben Gorman: Decision Trees in R using rpart
- Victor Zhou: Random Forests for Complete Beginners
- Bradley Boehmke & Brandon Greenwell: Hands-On Machine Learning with R
- Julia Kho: Why Random Forest is My Favorite Machine Learning Model
- JanBask: Training A Practical guide to implementing Random Forest in R with example
