Ensemble Methods In R

4 hours of instruction

This course covers an overview of ensemble learning methods like random forest and boosting. At the end of this course, students will be able to implement and compare random forest algorithm and boosting.

OBJECTIVES

1. Build random forest and gradient boosting models
2. Compare the different methods and evaluate per formance

PREREQUISITES

Students must have a foundation in classification models and model accuracy measures.

SYLLABUS & TOPICS COVERED

1. Random Forest
   • Ensemble methods use cases
   • Random Forests algorithm in a nutshell
   • Implement Random Forests on a dataset
2. Gradient Boosting
   • Gradient boosting algorithm in a nutshell
   • Implement gradient boosting on a dataset

SOFTWARE REQUIREMENTS

You will have access to an R-based Posit Cloud environment for this course. No additional download or installation is required.