This course on comparing tree-based models aims to teach learners how to fit decision trees, random forests, boosted trees, and other models in R. By examining various ways to fit these models and comparing them based on user-friendliness, accuracy, and speed, participants will gain the skills to work with rpart, randomForest, xgboost, and other packages. The teaching method involves a talk format with a detailed syllabus covering each model type and related topics. This course is intended for individuals interested in enhancing their knowledge of tree-based models and improving their data science skills in R.
Finding the Tallest Tree - Comparing Tree-Based Models
Overview
Syllabus
Introduction
Data
Single Tree
{rpart}
Random Forest
{ranger}
Boosted Trees
{gbm}
{C5.0}
{xgboost}
{lightgbm}
Other Models
{tidymodels}
Feature Engineering with {recipes}
{workflows}
Fit the Models
How did we do
Takeaways
Taught by
Data Science Dojo
