Pruning & Truncation
As per my understanding
Truncation: Stop the tree while it is still growing so that it may not end up with leaves containing very low data points. One way to do this is to set a minimum number of training inputs to use on each leaf.
Pruning is a technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that provide little power to classify instances.
Can somebody explain the in-detailed implementation of these techniques in GBDT frameworks like XGBoost/LightGBM.
Which parameters are used in implementing these techniques?