Live training

Live Code-Along: Machine Learning with XGBoost in Python

During this code-along, Lis Sulmont, Workspace Architect, will use XGBoost to predict booking cancellations with gradient boosting, a powerful machine learning technique! Through this, you’ll learn how to create, evaluate, and tune XGBoost models efficiently. This session will run for 1.5 hours, allowing you time to really immerse yourself in the subject, and includes short breaks and opportunities to ask questions throughout the training. Can't tune in live? Register and we’ll send you a link to the Live Code-Along recording afterwards.

Monday, November 15th, 11 AM ET
Register Now
Python

What will I learn?

You will learn how to:

  • Decide whether to use gradient boosting or not
  • Instantiate and customize XGBoost models
  • Use XGBoost's DMatrix to optimize computing performance
  • Evaluate models in XGBoost using the right metrics
  • Tune parameters in XGBoost to achieve the best results
  • Visualize trees in XGBoost to analyze feature importance

After this, you will be ready to use XGBoost to build your own model!

What should I prepare?

In case you haven’t already - create a free personal (not business) DataCamp account.

Lis Sulmont Headshot

Lis Sulmont

Workspace Architect

Lis is the Workspace Architect at DataCamp. She holds a Master's degree in Computer Science from McGill University with a focus in computer science education research and applied machine learning. Lis is passionate about teaching all things related to data and improving the accessibility of these topics.
Follow on LinkedIn