XGBoost is the flavour of the moment for serious competitors on kaggle. It was developed by Tianqi Chen and provides a particularly efficient implementation of the Gradient Boosting algorithm. Although there is a CLI implementation of XGBoost you’ll probably be more interested in using it from either R or Python. Below are instructions for getting it installed for each of these languages. It’s pretty painless.

Installing XGBoost in R

Installation in R is extremely simple.

> install.packages('xgboost')
> library(xgboost)

XGBoost is also supported as a model in caret, which is especially handy for feature selection and model parameter tuning.

Installing XGBoost in Python

Download the latest version of XGBoost from the github repository. The simplest way to do this is to grab the master zip archive. Unpack the archive, then become root and then execute the following:

# cd xgboost-master
# make
# cd python-package/
# python setup.py install --user

And you’re ready to roll:

import xgboost

Conclusion

Enjoy building great models with the absurdly powerful tool. I’ve found that it effortlessly consumes vast data sets that grind other algorithms to a halt. Get started by looking at some code examples. Also worth looking at are

• an Introduction to Boosted Trees;
• a tutorial showing how XGBoost was applied to the Otto Group Product Classification Challenge;
• Understanding Gradient Boosting (Part 1); and
• a presentation by Alexander Ihler.

The post Installing XGBoost on Ubuntu appeared first on Exegetic Analytics.