Algorithm 减少错误剪枝算法

我对这个算法有一个问题：

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.

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我不理解“永久删除导致验证集准确性最大提高的节点”这一部分。我们应该保留提高准确性的节点，删除增加错误率的节点。我错了吗？

我甚至不知道该算法适用于哪个领域，但我的理解是，提高精度的节点是那些未被修剪的节点，因此您引用的短语中没有矛盾。也许可以重新措辞

永久修剪修剪后导致最大错误的节点 提高验证集的准确性

让它更清楚