There are many techniques for tree pruning that differ in the measurement.

Feb 16, Trim the nodes of the decision tree in a bottom-up fashion. Post-pr u ning is done by replacing the node with leaf. If error improves after trimming, replace subtree by a leaf shrubhauling.barted Reading Time: 1 min.

Dec 11, 1. Post Pruning: This technique is used after construction of decision tree. This technique is used when decision tree will have very large depth and will show overfitting of model.

The Elements of Statistical Learning.

It Author: Akhil Anand. Apr 30, Post Pruning (Grow the tree and then trim it, replace subtree by leaf node) Reduced Error Pruning: 1. Holdout some instances from training data 2. Calculate misclassification for each of holdout set using the decision tree created 3. Pruning is done if parent node has errors lesser than child node; Cost Complexity or Weakest Link Pruning:Author: Shaily Jain.

Jul 26, Finding the optimal depth of a decision tree is accomplished by pruning. One way of pruning a decision tree is by the technique of reduced error pruning. Sep 21, By default, the Decision Tree function doesn’t perform any pruning and allows the tree to grow as much as it can.

We get an accuracy score of Author: Sarthak Arora.