Decision tree

Tree-based learning algorithms are one of the best supervised learning methods. They generally have stability over results, and great accuracy and generalization capacity to the out-sample dataset. They can map linear and nonlinear relationships quite well. It is generally represented in the form of a tree of variables and its results. The nodes in a tree are variables and end values are decision rules. I am going to use the package party to implement a decision tree. This package first need to be installed and loaded into the workspace using the following commands:

>install.packages("party")
>library(party)

The ctree() function is the function to fit the decision tree and it requires a formula and data as mandatory parameters and it has a few more optional variables. The normalized in-sample and normalized out-sample data does not have labels in the data so we have to merge labels in the data.

The following commands bind labels into the normalized in-sample and normalized...