Classification

Decision Tree

Machine Learning

Regression

January 8, 2021

What’s the difference between pre pruning and post pruning?

Decision trees are notoriously famous for overfitting. Pruning is a regularization method which penalizes the length of tree, i.e. increases the value of cost function.

Pruning is of two types:

  1. Post Pruning(Backward Pruning): Full tree is generated and then the non-significant branches are pruned/removed. Cross validation is performed at every step to check whether addition of the new branch leads to increase in accuracy. If not the branch is converted to leaf node.
  2. Pre Pruning(Forward Pruning): This approach stops the non-significant branches from generating. It terminates the generation of new branch based on the given condition.

by : Monis Khan

Quick Summary:

Decision trees are notoriously famous for overfitting. Pruning is a regularization method which penalizes the length of tree, i.e. increases the value of cost function. Pruning is of two types: Post Pruning(Backward Pruning): Full tree is generated and then the non-significant branches are pruned/removed. Cross validation is performed at every step to check whether addition […]