Machine learning with tree-based models in R

Course Authors: Gabriela de Queiroz & Erin LeDell

Description

Machine Learning is is the study of computer algorithms that improve automatically through experience; in other words, it's about getting computers to act without being explicitly programmed. One of the most popular approaches to machine learning, tree-based models, provides an attractive way to express knowledge and aid in decision-making. They have proven to be an effective solution for several machine learning problems in diverse domains, such as credit scoring, fraud detection and medical diagnostics. This course will teach you the basics of using tree-based models in R.

Learning objectives

• Learn the principles of tree-based machine learning models while using a combination of algorithms and software
• Learn how to effectively interpret and explain decisions made from a tree-based model
• Explore different use cases like identifying risky bank loans and predicting students' grades
• Build and evaluate models, including classification & regression trees, bagged trees, random forests, gradient boosting machines (GBM)
• Tune model parameters for optimal performance
• Evaluate variable importance to understand what variables most strongly predict the outcome