Control parameters in conditional inference trees

In the preceding recipe, we saw how to use ctree for control inference trees. We can tweak the algorithm by specifying the control parameters.

Getting ready

You have completed the previous recipe and now understand the ctree function.

How to do it...

Perform the following steps in R:

> ctree.model = ctree(churn ~ . , data = trainset, 
controls=ctree_control(testtype = "MonteCarlo", mincriterion =
0.90, minbucket = 15))> ctree.model

How it works...

This recipe is a continuation of the previous recipe, providing some control parameters using a controls argument to the ctree function. We said we are going to use the MonteCarlo simulation with minimum weight on node at 15 and mincriterion at 0.90. We can also use Bonferroni, Univariate, and Teststatistic in place of MonteCarlo. There are many other parameters that can be changed.

See also

For more, execute the following command:

> help(ctree_control)

Or use the following command:

> ?ctree_control...