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What does pruning refer to in a tree-based algorithm? Why is it used? |
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Answer» Response: When branches in a decision tree have weak predictive power, they are removed to reduce the complexity of the model or SOLUTION. They also INCREASE the predictive accuracy of a decision tree. This is referred to as pruning on decision trees which are basic tree-based approaches used in machine learning. Reduced error pruning is one of the simple methods that replace each node. This is used for optimizing the accuracy of the model/solution. An unpruned decision tree example: A pruned decision tree example: A pruned tree has fewer nodes and LESS sparsity compared to an unpruned tree. |
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