Pruning techniques refer to methods used to reduce the complexity of models, particularly in machine learning and data mining, by removing unnecessary branches from decision trees or simplifying algorithms without significantly affecting performance. These techniques aim to enhance the interpretability of models and improve their efficiency during classification and regression tasks, especially when working with large datasets.
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