XG Boost is a gradient boosted decision tree model that had proven to be a must have in your machine learning toolkit due to its efficiency and accuracy.
This repo contains a sample implementation of XGBoost with Sklearn Wrapper (i.e. XGBClassifier and XGBRegressor) for ease of implementation with Sklearn's GridSearchCV for parameter tuning.
In the sample implementation provided, the crucial parameters were tuned simultaeneously which might not be time efficient if the dataset is large. For large datasets, please tune the parameters separately for better time efficiency.