Decision trees are a powerful machine learning algorithm that can be used for both classification and regression tasks. They are easy to understand and interpret, and they can be used to build complex models without the need for feature engineering.
Once you have trained a decision tree model, you can use it to make predictions on new data. However, it can also be helpful to plot the decision tree to better understand how it works and to identify any potential problems.
In this blog post, we will show you how to plot decision trees in R using the
rpart.plotpackages. We will also provide an extensive example using the iris data set and explain the code blocks in simple to use terms.
Read on to see an example of how to do this.