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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. Decision trees are the most susceptible out of all the machine learning algorithms to overfitting and effective pruning can reduce this bushfelling.barted Reading Time: 7 mins. 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.
Oct 27, This also enables to modify some rules. This modification is called pruning in decision trees. It is a common technique in applied machine learning studies. We can apply pruning to avoid overfitting and to over-perform.
We will mention pruning techniques in this post. Pruning. Pruning can be handled as pre-pruning and bushfelling.barted Reading Time: 5 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.
In error complexity pruning is concern with calculating error cost of a node. Minimum Error pruning Estimated Reading Time: 3 mins.