It contains the importance of hyperparameter tuning to extract the best performance out of a machine learning model given the data.
In this repo, I have explored the use of the Hyperopt package for automatic hyperparameter tuning of an XGBoost classifier. Hyperparameter tuning is an important step in building accurate machine learning models, but it can be a challenging task that requires careful consideration of the available hyperparameters, prior experience, and domain knowledge. While traditional techniques such as grid search and randomized search are available in machine learning packages, they have their limitations. Grid search can be slow and computationally expensive, while randomized search can often perform worse than grid search. However, an automated tuning algorithm based on Bayesian optimization has shown promise compared to these traditional tuning algorithms. In this post, we will use Hyperopt, a Python library for Bayesian optimization, to perform hyperparameter tuning of an XGBoost classifier on a classification dataset.