Unlock the power of XGBoost by learning how to fine-tune its hyperparameters and discover its optimal modeling situations. This and more, when best-selling author and leading Python consultant Matt Harrison teams up with Jon Krohn for yet another jam-packed technical episode! Are you ready to upgrade your data science toolkit in just one hour? Tune-in now!

This episode is brought to you by Pathway, the reactive data processing framework (pathway.com/?from=superdatascience), by Posit, the open-source data science company (posit.co), and by Anaconda, the world's most popular Python distribution (superdatascience.com/anaconda). Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• Matt's book ‘Effective XGBoost’ [07:05]
• What is XGBoost [09:09]
• XGBoost's key model hyperparameters [19:01]
• XGBoost's secret sauce [29:57]
• When to use XGBoost [34:45]
• When not to use XGBoost [41:42]
• Matt’s recommended Python libraries [47:36]
• Matt's production tips [57:57]

Additional materials: www.superdatascience.com/681