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 ...