Random Forest Classifier Explained

With video explanation | Data Series | Episode 11.3

This episode builds upon the decision tree classification explained.

In the previous episode, we discussed how to build a decision tree classifier using information gain.

In this episode, we look at how we can use multiple decision trees (a forest) to classify data.

Building a Random Forest Classifier

Take a look at the following rice data:

Here we have two classes:

• 0-Gonen Rice
• 1-Jasmine Rice

Building a simple decision tree on this data we get the following:

With an accuracy of 0.71 (2dp)