title: Reduced Error Pruning Algorithm
body: <p>I have a question about this algorithm:</p>
  
  <pre><code>Partition training data in “grow” and “validation” sets.
  Build a complete tree from the “grow” data.
  Until accuracy on validation set decreases do:
      For each non-leaf node, n, in the tree do:
          Temporarily prune the subtree below n and replace it with a leaf labeled with
                                                the current majority class at that node.
          Measure and record the accuracy of the pruned tree on the validation set.
      Permanently prune the node that results in the greatest increase in accuracy on the
                                                                           validation set.
  </code></pre>
  
  <p>I don't understand the part "Permanently prune the node that results in the greatest increase in accuracy on the validation set." We are supposed to keep the nodes that increase the accuracy and prune those that increase the error rate. Am I wrong?</p>