Answer / Deepika Singh

Pruning in Decision Trees is a technique that aims to improve the predictive accuracy and generalization of a tree by removing less important sub-trees. Pruning reduces overfitting (when a model learns too much detail from the training data) by making the tree smaller and simpler, thus increasing its ability to make accurate predictions on unseen data.

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