681: XGBoost: The Ultimate Classifier — with Matt Harrison
#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=superdatasc..., 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
771: Gradient Boosting: XGBoost, LightGBM and CatBoost — with Kirill Eremenko
Top Five Tricks for Coding in Pandas — with Matt Harrison
TabPFN: Deep Learning for Tabular Data is Here! (Prof. Frank Hutter explains)
Gradient Boosting : Data Science's Silver Bullet
Nobody Explained the Schrödinger Equation Like THIS!
Turing Award Winner: Disagreeing with Google, Postgres, Future Problems | Mike Stonebraker
17. Learning: Boosting
General relativity from first principles – Adam Brown
Time Series Forecasting with XGBoost - Advanced Methods
SDS 557: Effective Pandas — with Matt Harrison
Pedro Tabacof - Unlocking the Power of Gradient-Boosted Trees (using LightGBM) | PyData London 2022
What is XGBoost
XGBOOST in Python (Hyper parameter tuning)
Creator of C++: Bell Labs, Negative Overhead Abstraction, Mistakes | Bjarne Stroustrup
Richard Sutton – Father of RL thinks LLMs are a dead end
A Complete Introduction to XGBoost for Machine Learning Engineers
Understanding XGBoost From A to Z!
863: TabPFN: Deep Learning for Tabular Data (That Actually Works!) — with Prof. Frank Hutter
When to Use XGBoost
