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When ccp_alpha is set to zero and keeping the other default parameters of DecisionTreeClassifier, the tree overfits, leading to a % training accuracy and 88% testing accuracy. As alpha increases, more of the tree is pruned, thus creating a decision tree that generalizes better.
In this example, setting ccp_alpha= maximizes the testing accuracy. Feb 16, Post-pruning is also known as backward pruning. In this, first generate the decision tree and then r e move non-significant branches. Post-pruning a decision tree implies that we begin by generating the (complete) tree and then adjust it with the aim Estimated Reading Time: 3 mins.
Pre-pruning methods are considered to be more efficient because they do not induce an entire set, but rather trees remain small from the start.
Jul 04, Decision trees are the most susceptible out of all the machine learning algorithms to overfitting and effective pruning can reduce this likelihood. This post will go over two techniques to help with overfitting - pre-pruning or early stopping and post-pruning with treenotch.barted Reading Time: 7 mins. Dec 11, 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.
It Author: Akhil Anand. - Insufficient number of training records in the region causes the decision tree to predict the test examples using other training records that are irrelevant to the classification task 11/26/ 6 11 Some post pruning methods need an independent data set: “Pruning Set” File Size: KB.