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

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The home page of XGBoost is: https://xgboost.ai

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