In this course you'll learn how to work with tree-based models in R. This course covers everything from using a single tree for regression or classification to more advanced ensemble methods. You'll learn to implement bagged trees, Random Forests, and boosted trees using the Gradient Boosting Machine, or GBM. These powerful techinques will allow you to create high performance regression and classification models for your data.
This chapter covers supervised machine learning with classification trees.
In this chapter you'll learn how to use a single tree for regression, instead of classification.
In this chapter, you will learn about Bagged Trees, an ensemble method, that uses a combination of trees (instead of only one).
In this chapter, you will learn about the Random Forest algorithm, another tree-based ensemble method. Random Forest is a modified version of bagged trees with better performance. Here you'll learn how to train, tune and evaluate Random Forest models in R.
In this chapter, you will see the boosting methodology with a focus on the Gradient Boosting Machine (GBM) algorithm, another popular tree-based ensemble method. Here you'll learn how to train, tune and evaluate GBM models in R.
Gabriela de Queiroz
Data Scientist and founder of R-Ladies
Gabriela de Queiroz is a Data Scientist and the founder of R-Ladies, a world-wide organization for promoting diversity in the R community.
She likes to mentor and share her knowledge through mentorship programs, tutorials and talks. She has worked in several startups and where she built teams, developed statistical models and employed a variety of techniques to derive insights and drive data-centric decisions. She holds 2 Master’s: one in Epidemiology and one in Statistics. Follow her at @gdequeiroz on Twitter and find out more about R-Ladies at rladies.org.
Chief Machine Learning Scientist at H2O.ai