What is XGBoost? A quick and easy introduction

This video is a super quick guide to the basics of XGBoost or the eXtreme Gradient Boosting library. XGBoost includes several optimization techniques for Gradient Boosting, a popular and powerful classical machine learning (ML) technique for performing regression, classification, or clustering on tabular data. Learn how XGBoost can help with optimal construction of decision trees, uses similarity score for determining splits, and makes the gradient boosting process less computationally intensive. The video also details several advantages of XGBoost including built-in L1 and L2 regularization for model generalizability, easy decision tree pruning for model simplicity, seamless handling of missing data for wider applicability, built-in cross validation for result replicability, easy interface and cross language support for increased developer productivity, and easier deployment across compute resources for performance scalability. To continue you learning journey, check out the awesome XGBoost page on GitHub for examples and tutorials on several XGBoost use cases.

For more details on Gradient Boosting, check out our introductory video on the topic:    • What is Gradient Boosting? A quick and eas...  

Awesome XGBoost GitHub: github.com/dmlc/xgboost/tree/master/demo

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