Digital Resources for Chapter 10: Tree-Based Models/Random Forest

Below are digital resources that complement the book Practical Machine Learning with R: Tutorials and Case Studies.

Random Forests Part 1 - Building, Using and Evaluating from StatQuest by Josh Starmer

This video is Part 1 of a video series about Random Forest. The video explains the basics of Random Forest, including an introduction to Bootstrapping.

Tuning Hyper Parameters in a Random Forest Model

This blog post by Carsten Lange shows how the hyper-parameters of a Random Forest model that estimates COVID-19 vaccination rates in 2021 can be tuned using the 10-Step Tuning template.

An Implementation and Explanation of the Random Forest in Python

A guide for using and understanding the random forest by building up from a single decision tree.

Ordinal data models

This tutorial aims to explore the most popular models used to predict an ordered response variable. We will use the heart disease data uploaded from kaggle website, where our response will be the chest pain cp variable instead of the target variable used usually. Including Ordinal Random forst model.