title: Decision Tree producing 0.5 value at split for binary variables
body: <p>I have a lot of categorical variables which I converted to dummy variable by one hot encoding for each of the features and plotted a decision tree in R. I am not able to understand the output. Can someone please help me to explain how a split a a node can have 0.5 as value when the variable has only 0 or 1. Code and decision tree below</p>
  
  <pre><code>mtree &lt;- rpart(Converted~., data = training, method="class",
                 control = rpart.control(minsplit = 20, minbucket = 7, maxdepth = 5,
                                         usesurrogate = 2, xval =10 ))
  
  rpart.plot(mtree, # middle graph
             type=4,
             extra=101, 
             box.palette="GnBu",
             branch.lty=3, 
             shadow.col="gray", 
             nn=TRUE,cex = 1
  )
  </code></pre>
  
  <h3><a href="https://i.stack.imgur.com/XQurz.png" rel="nofollow noreferrer">Decision Tree</a>Tree</h3>
  
  <p>Example : For the variable Month June which is one hot encoded how do I explain Month.June >=0.5</p>