What is a Decision Tree in R and How to Use It?

Article Complete

CheatSheet: Decision Tree in R

Explore the basics of decision trees in R. Download our cheatsheet and subscribe for more insights on machine learning techniques!

AIM Media House·Published 2020-10-18T05:30:00.000+05:30Updated 2024-12-31T05:30:00.000+05:30

Key takeaways

A decision tree is a machine learning algorithm that visualises inputs and outcomes in a tree structure.
Decision trees can be utilised for both classification and regression tasks.
The structure includes nodes representing events and edges reflecting decisions, culminating in a leaf node for the final outcome.

5 min

FollowPreferred Source Google News WhatsApp Telegram

A decision tree is a machine learning algorithm that represents the inputs and outcomes in the form of a tree. They are used for building both classification and regression models.

The decision tree contains nodes and edges which represent the events and decisions respectively. The first node is called the root node and branches into internal nodes. The final output node is called a leaf node. The leaf node represents the decision of the model.

To download the cheatsheet, login below and subscribe to our YouTube channel.