3 Methods for Hyperparameter Tuning with XGBoost

In this video we will cover 3 different methods for hyper parameter tuning in XGBoost. These include: 1. Grid Search 2. Randomized Search 3. Bayesian Optimization The break-down of the video is as follows: 00:00 Video Introduction 00:38 What are Hyperparameters? 02:25 Number of Weak Learners 03:40 Learning Rate 04:34 Maximum Depth 05:31 L1 Regularization 06:43 L2 Regularization 07:52 Methods for Hyperparameter Tuning 11:25 Start Jupyter Notebook 14:08 Grid Search 17:07 Randomized Search 20:14 Bayesian Optimization 22:29 Concluding Remarks The best way to keep up-to-date with my video/blog content is to sign up for my monthly Newsletter! Please visit: https://insidelearningmachines.com/newsletter/ to register. The notebook presented here can be found at: https://github.com/insidelearningmachines/Blog/blob/main/3%20Methods%20for%20Hyperparameter%20Tuning%20with%20XGBoost.ipynb The homepage of my blog is: https://insidelearningmachines.com The home page of XGBoost is: https://xgboost.ai Other social media includes: Twitter: https://twitter.com/inside_machines Facebook: https://www.facebook.com/Inside-Learning-Machines-112215488183517 #machinelearning #datascience #boosting #xgboost #insidelearningmachines