Optimizing XGBoost: A Guide to Hyperparameter Tuning

<p>Hyperparameter tuning is important because the performance of a machine learning model is heavily influenced by the choice of hyperparameters. Choosing the right set of hyperparameters can lead to better model performance, while choosing the wrong set can lead to poor performance. Additionally, when a model has too many hyperparameters, it can be difficult or even impossible to find the best set of hyperparameters manually.</p> <h1>Different types of hyperparameters in XGBoost</h1> <p>In XGBoost, there are two main types of hyperparameters: tree-specific and learning task-specific.</p> <p><a href="https://medium.com/@rithpansanga/optimizing-xgboost-a-guide-to-hyperparameter-tuning-77b6e48e289d"><strong>Learn More</strong></a></p>