Dive into a comprehensive video tutorial on XGBoost, a powerful machine learning algorithm. Explore gradient-boosting principles, including initialization, loss functions, and decision trees. Learn data preparation techniques, parameter tuning strategies, and model evaluation methods. Watch a real-world implementation and discover how to deploy XGBoost models for practical applications. Gain insights into XGBoost's attributes, benefits, and limitations, and understand its differences from other boosting algorithms and random forests. Follow along with hands-on demonstrations and practical steps to perform XGBoost effectively. By the end of this hour-long tutorial, acquire the knowledge and tools necessary to excel in using XGBoost for various machine learning tasks.
Introduction to XGBoost - Machine Learning Tutorial
Overview
Syllabus
Beginning
Introduction to XGBoost
Attributes, Benefits, and Limitations of XGBoost
Introduction to Gradient Boosting
XGBoost vs Gradient Boost vs Ada Boosting
XGBoost vs Random Forest
XGBoost - Working
Steps to Perform XGBoost
XGBoost Hands-on
XGBoost - Summary
Taught by
Great Learning
