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Download scientific diagram | decision tree of whether the grades after pruning fail

The application method of big data of data mining algorithm in college. Decision tree pruning helps to improve the performance and interpretability of decision trees by reducing their complexity and avoiding overfitting Proper pruning can lead to simpler and more robust models that generalize better to unseen data. Pruning is a crucial technique to prevent overfitting by reducing the complexity of the tree

This tutorial explores different pruning techniques and provides code examples to demonstrate their application Pruning involves selectively removing branches or nodes from a decision tree to simplify it A simpler tree generalizes better to new data. Decision trees are tree data structures that are generated using learning algorithms for the purpose of classification and regression

One of the most common problem when learning a decision tree is to learn the optimal size of the resulting tree that leads to a better accuracy of the model.

− decision trees aim to find a hierarchical structure to explain how different areas in the input space correspond to different outcomes − they tend to be insensitive to normalization issues and tolerant toward many correlated or noisy attributes. Our algorithm reduces the tree size while preserving the accuracy of the unpruned tree.

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