Grid search, random search, and Bayesian optimization are techniques for tuning hyperparameters on machine learning models. This tutorial discusses how to tune XGBoost hyperparameters using Python. You will learn

What are the differences between grid search, random search and Bayesian optimization (Hyperopt)?
How can I use random cross-validation to tune the hyperparameters for the XGBoost model?
How to use Bayesian optimization Hyperopt to tune the hyperparameters for the XGBoost model?
How do you compare the results of grid search, random search and Bayesian optimization Hyperopt?

Time codes
0:00 – Introduction
0:37 – Step 0: Grid Search vs. Random Search Vs. Bayesian optimization
1:38 – Step 1: Install and import libraries
2:09 – Step 2: Read data
2:31 – Step 3: Train test split
2:45 – Step 4: Standardization
3:45 – Step 5: XGBoost classification without hyperparameter tuning
7:12 – Step 6: Grid search for XGBoost
9:39 – Step 7: Random search for XGBoost
10:51 – Step 8: Bayesian Optimization Hyperopt for XGBoost

️ Blog post with code for this video: https://medium.com/grabngoinfo/hyperparameter-tuning-for-xgboost-91449869c57e
Code notebook: https://colab.research.google.com/drive/18ooFZ4e7cW_zpbvwhBzzhWxCze0Mi6LA#scrollTo=1-FxiavJMirS
GrabNGoInfo Machine Learning Tutorials Inventory: https://medium.com/grabngoinfo/grabngoinfo-machine-learning-tutorials-inventory-9b9d78ebdd67

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