Tuning Model Hyper-Parameters for XGBoost and Kaggle
Properly setting the parameters for XGBoost can give increased model accuracy/performance. This is a very important technique for both Kaggle competitions and data science in general. In this video I will show how I automated one popular technique for XGBoost. Code is here: https://github.com/jeffheaton/jh-kagg...

▶︎
XGBoost for Multi-Class Classification with Python | Step-by-Step with Hyperparameter Tuning

▶︎
How to use Feature Engineering for Machine Learning, Equations

▶︎
681: XGBoost: The Ultimate Classifier — with Matt Harrison

▶︎
APAC - Quantitative Research Masterclass 2025

▶︎
Creating Machine Learning Ensembles for Kaggle Competitions

▶︎
Deep Dive into LLMs like ChatGPT

▶︎
Transformers, the tech behind LLMs | Deep Learning Chapter 5

▶︎
Pedro Tabacof - Unlocking the Power of Gradient-Boosted Trees (using LightGBM) | PyData London 2022

▶︎
XGBOOST in Python (Hyper parameter tuning)

▶︎
XGBoost A Scalable Tree Boosting System June 02, 2016

▶︎
XGBoost Made Easy | Extreme Gradient Boosting | AWS SageMaker

▶︎
Jaroslaw Szymczak - Gradient Boosting in Practice: a deep dive into xgboost

▶︎
Can one do better than XGBoost? - Mateusz Susik

▶︎
How AI Cracked the Protein Folding Code and Won a Nobel Prize

▶︎
Using Large Language Models | Build Your Own LLM Workshop #1

▶︎
GridSearchCV- Select the best hyperparameter for any Classification Model

▶︎
Hyperparameter Optimization for Xgboost

▶︎
XGBoost: How it works, with an example.

▶︎
Visualizing transformers and attention | Talk for TNG Big Tech Day '24

▶︎
