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Did you know why over-pruning can cause decision trees to lose important information?
In machine learning and search algorithms, pruning is a data compression technique that aims to reduce the size of a decision tree by removing uncritical and redundant tree nodes. This approach not only reduces the complexity of the final classifier but also improves prediction accuracy by reducing overfitting. However, when applying pruning strategies, excessive pruning may cause the decision tree to lose some important information, thereby affecting the predictive ability of the model.
Excessive pruning may cause the model to lose the capture of important structural information in the sample space.
In decision tree models, a key issue is the optimal size of the final tree. If the tree is too large, it may overfit the training data and reduce the generalization ability to new samples. In contrast, if the tree model is too small, it may not be able to capture the essential structure of the sample space. This contradiction makes it difficult to adjust the model, because it is difficult to judge whether the addition of a single additional node will significantly reduce the error rate. This is the so-called horizon effect.
Pruning is divided into two categories: pre-pruning and post-pruning. Prepruning avoids complete derivation of the training set by replacing some stopping criteria, ensuring that the tree remains small in size from the start. However, pre-pruning methods often also face horizon effects and cannot avoid premature tree termination. In contrast, post-pruning is a more common way to simplify a tree, reducing the complexity of the tree by replacing nodes and subtrees with leaves.
Post-pruning can significantly reduce tree size and improve classification accuracy for unseen objects, although accuracy on the training set may decrease.
The specific method of pruning can be divided into "top-down" and "bottom-up" according to the way it is processed on the tree. In the bottom-up pruning method, the starting point of the program is set at the end of the tree, and the relevance of each node is determined by traversing upward; if a node is not important to the classification result, the node will be eliminated. . The advantage of this approach is that no important subtrees are missed. The top-down pruning method starts from the root of the tree and also performs dependency checks, but may result in the loss of the entire subtree, regardless of whether it is important.
Among the pruning algorithms, simple reduced error pruning is the most basic form. Under this approach, starting at the leaves of the tree, each node is replaced with its most common category, and this change is retained if it does not affect prediction accuracy. Although this method seems simple, it is very effective and saves computing time.
Cost complexity pruning creates a series of trees, where each step is performed by removing a subtree from the previous tree and replacing it with a leaf node. This process is repeated multiple times to determine the best tree shape, and ultimately the tree with the best accuracy as measured by the test set or cross-validation is selected.
In neural networks, pruning is also applied to remove entire neurons or layers of neurons to further simplify the model and preserve key features. And just like the case of decision trees, if unnecessary parts are pruned too much, the overall prediction effect may be harmed.
Implementing a moderate pruning strategy can effectively improve model performance, but excessive pruning may damage the performance of the decision tree.
Therefore, we must strike a balance during the pruning process and carefully select which nodes are worth retaining and which can be removed in order to simplify its structure while maintaining the accuracy of the model. Such a decision is not only related to the basic principles of the algorithm, but also a profound technical art in machine learning. So, in this process, how should we more effectively balance the contradiction between algorithm simplification and performance?
