681: XGBoost: The Ultimate Classifier, with Matt Harrison
Super Data Science: ML & AI Podcast with Jon Krohn
In-depth discussion on XGBoost and related Python libraries
The chapter explores the XGBoost algorithm and its hyperparameters, detailing how to optimize model depth, regularization, and learning rate for improved classification accuracy. It also covers various Python libraries such as Pandas, Scikit-learn, Yellowbrick, and XGB FIR, for tasks like data preprocessing, pipelining, performance visualization, and model explainability.
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