Introduction to Classification Trees and Random Forests in R

Abstract

“Random Forests” are used everywhere, and for good reason! Random Forest is a powerful and versatile machine learning algorithm that grows and combines multiple decision trees to create a “forest”. It sounds very complex, but learning to use them is very intuitive, especially if you have a USNW codeRs workshop to help you.

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

--- date: "2021-07-30" title: "Introduction to Classification Trees and Random Forests in R" author: José R. Ferrer-Paris links: - icon: video # icon_pack: fas name: Workshop Recording url: https://bit.ly/2VrCFh4 - icon: github icon_pack: fab name: github Material url: https://github.com/UNSW-codeRs/workshop-random-forests --- <img src="random_forest.png" width='100%' style = "margin-left: 0px; margin-right: 0px; float:right;" > ## Abstract "Random Forests" are used everywhere, and for good reason! Random Forest is a powerful and versatile machine learning algorithm that grows and combines multiple decision trees to create a "forest". It sounds very complex, but learning to use them is very intuitive, especially if you have a **USNW codeRs workshop** to help you. ## Resources created by JR [This GitHub repository](https://github.com/UNSW-codeRs/workshop-random-forests)! 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](https://www.freecodecamp.org/news/how-to-use-the-tree-based-algorithm-for-machine-learning/) - Evan Muzzall and Chris Kennedy: [Introduction to Machine Learning in R](https://dlab-berkeley.github.io/Machine-Learning-in-R/slides.html) - Github link: [Machine Learning in R](https://github.com/dlab-berkeley/Machine-Learning-in-R) - Github link: [Machine Learning with Tidymodels](https://github.com/dlab-berkeley/Machine-Learning-with-tidymodels) - Dave Tang: [Building a classification tree in R](https://davetang.org/muse/2013/03/12/building-a-classification-tree-in-r) - Zach @ Statology: [How to Fit Classification and Regression Trees in R](https://www.statology.org/classification-and-regression-trees-in-r/) - Ben Gorman: [Decision Trees in R using rpart](https://www.gormanalysis.com/blog/decision-trees-in-r-using-rpart/) - Victor Zhou: [Random Forests for Complete Beginners](https://victorzhou.com/blog/intro-to-random-forests/) - Bradley Boehmke & Brandon Greenwell: [Hands-On Machine Learning with R](https://bradleyboehmke.github.io/HOML/random-forest.html) - Julia Kho: [Why Random Forest is My Favorite Machine Learning Model](https://towardsdatascience.com/why-random-forest-is-my-favorite-machine-learning-model-b97651fa3706) - JanBask: [Training A Practical guide to implementing Random Forest in R with example](https://www.janbasktraining.com/blog/random-forest-in-r/)