There is always a bit of luck involved when selecting parameters for Machine Learning model training. Lately, I have worked with gradient boosted trees and XGBoost in particular. We are using XGBoost in the enterprise to automate repetitive human tasks. While training ML models with XGBoost, I created a pattern to choose parameters, which helps me to build new models quicker. I will share it in this post, and hopefully you will find it useful too.
I'm using the Pima Indians Diabetes Database for the training. CSV data can be downloaded from here.