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 ...
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