Further Reading To continue learning related concepts, check out this great.
Jun 14, Pruning also simplifies a decision tree by removing the weakest rules. Pruning is often distinguished into: Pre-pruning (early stopping) stops the tree before it has completed classifying the training set, Post-pruning allows the tree to classify the training set perfectly and then prunes the tree. We will focus on post-pruning in this bushleaning.bar: Edward Krueger. Jul 04, The learning above boils down to a few guidelines: For best accuracy, minimum error pruning without early stopping is usually a good choice.
For a compromise between accuracy and an interpretable tree, try smallest tree pruning without early stopping.
To produce an even smaller tree or Estimated Reading Time: 7 mins. Since each distinct path through the decision tree node produces a distinct rule, the pruning decision regarding that attribute test can be made differently for each path. In contrast, if the tree itself were pruned, the only two choices would be: Remove the decision node completely, or Retain it.