How does decision tree pruning help in reducing overfitting?
In machine learning, decision tree pruning is an important technique that reduces overfitting while improving the model's ability to generalize new data. Overfitting is when a decision-tree captures noise from the training dataset and becomes too complex. It also becomes highly specific to that data. Unpruned trees may achieve 100% accuracy in the training data, but perform poorly when compared to unseen data. By simplifying the structure of the tree, pruning can address this problem.
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