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, by Posit, the open-source data science company, and by Anaconda, the world's most popular Python distribution. 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

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