Skip to Main Content
Hands-On Ensemble Learning with R
book

Hands-On Ensemble Learning with R

by Prabhanjan Narayanachar Tattar
July 2018
Beginner to intermediate content levelBeginner to intermediate
376 pages
9h 1m
English
Packt Publishing
Content preview from Hands-On Ensemble Learning with R

Ensembling by voting

Ensembling by voting can be used efficiently for classification problems. We now have a set of classifiers, and we need to use them to predict the class of an unknown case. The combining of the predictions of the classifiers can proceed in multiple ways. The two options that we will consider are majority voting, and weighted voting.

Majority voting

Ideas related to voting will be illustrated through an ensemble based on the homogeneous base learners of decision trees, as used in the development of bagging and random forests. First, we will create 500 base learners using the randomForest function and repeat the program in the first block, as seen in Chapter 4, Random Forests. Ensembling has already been performed in that chapter, ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Hands-On Deep Learning with R

Hands-On Deep Learning with R

Rodger Devine, Michael Pawlus
Learning Bayesian Models with R

Learning Bayesian Models with R

Hari Manassery Koduvely
Advanced Machine Learning with R

Advanced Machine Learning with R

Cory Lesmeister, Dr. Sunil Kumar Chinnamgari

Publisher Resources

ISBN: 9781788624145Supplemental Content