Bayesian Optimization using xgboost and sklearn API
Simple test scripts for optimal hyperparameter of xgboost using bayesian optimization.
Original bayesian optimization code is from https://github.com/fmfn/BayesianOptimization and all credit for this work goes to the original author.
Example 1. is based on the otto dataset from Kaggle, this remains in memory. (https://www.kaggle.com/c/otto-group-product-classification-challenge)
Example 2. is based on Avazu click prediction dataset from Kaggle and requires the 'distributed' version of xgboost. (https://www.kaggle.com/c/avazu-ctr-prediction)
To get this running create a data/otto and data/avazu dir and download the datasets into the respective directories and unzip / untar the files.
Dependencies:
- Scipy
- Numpy
- Scikit-Learn
- xgboost (https://github.com/dmlc/xgboost)
References: