What is the purpose of pruning a decision tree?
Anyone outside of the field of machine learning may not understand this question. It appears to be more agriculture rather than machine learning related. However, as an engineer in this field, you should immediately recognize the concept and be able to discuss it. Since this is a technical question, keep your answer brief and to the point. You should also anticipate follow-up questions, indicating that this is an important process used by the organization.
"Pruning a decision tree refers to the process of removing branches that have weak predictive outcomes to reduce the complexity of the model and increase the accuracy of the decision tree. Approaches to this include reduced error pruning and cost complexity pruning, both of which can be performed either top-down or bottom-up. The process involves removing a branch and then testing the model to determine if the accuracy was increased or remained the same. The branch can be reinserted if its removal does not affect the accuracy of the model."
