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 for R
Installation in R is extremely simple.
> install.packages('xgboost') > library(xgboost)
It’s also supported as a model in caret, which is especially handy for feature selection and model parameter tuning.
Installing for Python
This might be as simple as
$ pip install xgboost
If you run into trouble with that, try the alternative approach below.
# cd xgboost-master # make # cd python-package/ # python setup.py install -user
And you’re ready to roll:
If you run into trouble during the process you might have to install a few other packages:
# apt-get install g++ gfortran # apt-get install python-dev python-numpy python-scipy python-matplotlib python-pandas # apt-get install libatlas-base-dev
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