How to Tune the Number and Size of Decision Trees with XGBoost in Python - Machine Learning Mastery

Gradient boosting involves the creation and addition of decision trees sequentially, each attempting to correct the mistakes of the learners that came before it. This raises the question as to how many trees (weak learners or estimators) to configure in your gradient boosting model and how big each tree should be. In this post you will discover how to design a systematic experiment to select the number and size of decision trees to use on your problem. How to Tune the Number and Size of Decision Trees with XGBoost in Python Photo by USFWSmidwest, some rights reserved. XGBoost is the high performance implementation of gradient boosting that you can now access directly in Python.