Working my way thru fully grasping XGBoost for machine learning; adapting different materials and notebooks.
I will be pulling heavily from these books listed in the references
- Wade, C. (2020). Hands-On Gradient Boosting with XGBoost and scikit-learn: Perform accessible machine learning and extreme gradient boosting with Python (1st ed.). Packt Publishing.
- Harrison, M. (2023). Effective XGBoost: Tuning, Understanding, and Deploying Classification Models. MetaSnake.
- Harrison, M. (2021). Effective Pandas: Patterns for Data Manipulation. Independently published.
There is a discord channel for this project. You can join the conversation there by clicking on this link.