XGBRegressor

Overview

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

Installing XGBoost for Python

Follow instructions here

Resources

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