# What is Pruning & Truncation in Decision Trees?

## 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?

Your understanding is correct. xgboost has nice explanation in the docs.
As for dessert, here's catboost paper.