Skip to Main Content
Pro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and R
book

Pro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and R

by V Kishore Ayyadevara
June 2018
Intermediate to advanced content levelIntermediate to advanced
379 pages
7h 33m
English
Apress
Content preview from Pro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and R
© V Kishore Ayyadevara 2018
V Kishore AyyadevaraPro Machine Learning Algorithms https://doi.org/10.1007/978-1-4842-3564-5_5

5. Random Forest

V Kishore Ayyadevara1 
(1)
Hyderabad, Andhra Pradesh, India
 

In Chapter 4, we looked at the process of building a decision tree. Decision trees can overfit on top of the data in some cases—for example, when there are outliers in dependent variable. Having correlated independent variables may also result in the incorrect variable being selected for splitting the root node.

Random forest overcomes those challenges by building multiple decision trees, where each decision tree works on a sample of the data. Let’s break down the term: random refers to the random sampling of data from original dataset, and forest refers ...

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

Machine Learning Algorithms

Machine Learning Algorithms

Giuseppe Bonaccorso

Publisher Resources

ISBN: 9781484235645Purchase LinkPublisher Website