减少错误修剪算法

Question

我对此算法有疑问：

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.

我不理解“永久修剪节点会导致验证集上的准确性增加最多的部分”。我们应该保留节点以提高准确性并修剪那些增加错误率的节点。我错了吗？

Answer 1

我甚至不知道这个算法适用于哪个领域，但是我的理解是提高准确性的节点是不修剪的节点，所以在你的短语中没有矛盾引用。也许它可以改写

  

永久修剪节点，当修剪时，该节点会导致最大的节点   提高验证集的准确性

使其更清晰。