If you've spent any time on Kaggle, you've seen XGBoost win. Over and over. Structured data competition? XGBoost. Tabular data problem? XGBoost. Real-world ML pipeline? XGBoost.

It's not hype. It genuinely is that good on most problems with structured data.

But a lot of people use it without understanding why it works. They just copy the code, tune a few numbers, and hope for the best. This post fixes that.

What You'll Learn Here

The difference between bagging and boosting

How gradient boosting works step by step

What makes XGBoost faster and better than basic gradient boosting

How to train XGBoost for classification and regression

The most important hyperparameters and what they actually do

Early stopping so you never have to guess the right number of trees

Bagging vs Boosting: The Core Difference

Random Forest uses bagging . Trees are built independently, in parallel, on random subsets of data. Final answer = average of all trees.

XGBoost uses boosting .…