A simple implementation to regression problems using Python 2.7, scikit-learn, and XGBoost. Bulk of code from Complete Guide to Parameter Tuning in XGBoost

XGBRegressor is a general purpose notebook for model training using XGBoost. It contains:

• Functions to preprocess a data file into the necessary train and test set dataframes for XGBoost
• Functions to convert categorical variables into dummies or dense vectors, and convert string values into Python compatible strings
• Additional user functionality that allows notification updates to be sent to a user's chosen Slack channel, so that you know when your model has finished training
• Implementation of sequential hyperparameter grid search via the scikit-learn API
• Implementation of early stopping via the Learning API

Follow instructions here

Here are some additional resources if you are looking to explore XGBoost and its various APIs more extensively:

1. Introduction to Boosted Trees and the XGBoost algorithm
2. The Python API documentation for XGBoost
3. Complete Guide to Parameter Tuning in XGBoost
4. scikit-learn's Gradient Boosting Classifer documentation
5. scikit-learn's GridSearchCV documentation
6. Tong He's XGBoost presentation