By Prakhar Katiyar.

In Pre-pruning, we use parameters like ‘max_depth’ and ‘max_samples_split’. But here we prune the branches of decision tree using cost_complexity_pruning technique. ccp_alpha, the cost complexity parameter, parameterizes this pruning technique. ccp_alpha gives minimum leaf value of decision tree and each ccp_alpha will create different – different classifier and choose the best out of it.

Jul 04, In machine learning and data mining, pruning is a technique associated with decision trees. Pruning reduces the size of decision trees by removing parts of the tree that do not provide power to classify instances.

It may be the case that the accuracy of the assignment on the train set deteriorates, but the accuracy of the classification properties of the tree increases overall.

Decision trees are the most susceptible out of all the machine learning algorithms to overfitting and effective pruning can reduce this takedownstump.barted Reading Time: 7 mins. Feb 16, Post-pruning techniques in decision tree Reduced Error Pruning.

This method was proposed by Quinlan. It is simplest and most understandable method in decision Error Complexity Pruning.

One of the questions that arises in a decision tree algorithm is the optimal size of the final tree.

In error complexity pruning is concern with calculating error cost of a node. Minimum Error pruning Estimated Reading Time: 3 mins.