Answer / Lucky Tyagi

A decision tree can be pruned (reduced in size) to improve its predictive accuracy and prevent overfitting. Pruning involves removing branches or nodes that contribute little to the overall accuracy of the tree, usually by minimizing the cost function (such as cross-entropy for classification problems). Common methods for pruning include reduced error pruning, cost complexity pruning, and minimum description length pruning.

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