#XGBoost #PythonLibraries #XGBoostClassifier 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 @JonKrohnLearns 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 (https://pathway.com/?from=superdatascience), by Posit, the open-source data science company (https://posit.co), and by Anaconda, the world's most popular Python distribution (https://superdatascience.com/anaconda). Interested in sponsoring a SuperDataScience Podcast episode? Visit https://jonkrohn.com/podcast for sponsorship information. In this episode you will learn: • [00:00:00] Introduction • [00:04:54] Matt's book ‘Effective XGBoost’ • [00:06:58] What is XGBoost • [00:16:49] XGBoost's key model hyperparameters • [00:27:45] XGBoost's secret sauce • [00:32:33] When to use XGBoost • [00:39:30] When not to use XGBoost • [00:45:24] Matt’s recommended Python libraries • [00:55:45] Matt's production tips Additional materials: https://www.superdatascience.com/681