XGBoost Quick Start Guide with an Iris Data Case Study
This chapter acts as a quick start guide that’s designed to give you hands-on experience using XGBoost in Python. The purpose of this chapter is to get you familiar with the code so that you can train a model with XGBoost and then use that model to make a prediction (inference). By the end of this chapter, you will have built a classifier model using XGBoost and be able to use that code as a foundation for similar classification problems. In Chapter 4, you’ll practice using XGBoost to make predictions and learn what code stays the same when switching datasets and what needs to change. In the What’s next? section, we’ll guide you to other parts of this book based on your interest in understanding more theory or practical use cases.
In this chapter, we’ll cover the following main topics:
- Downloading and installing the XGBoost package
- Ingesting and exploring data
- Preparing data...
