After building a decision tree a tree pruning step can be performed to reduce the size of the decision tree. Decision trees that are too large are susceptible to a phenomenon called as overfitting. Pruning helps by trimming the branches that reflects anomalies in the training data due to noise or outliers and helps the initial tree in a way that improves the generalization capability of the tree. Such methods typically use statistical measures to remove the least reliable branches, generally resulting in faster classification and an improvement in the ability of the tree to correctly classify independent test data.
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