Talk (and code) from my talk @ Lancaster Data Science Group on "Parameter tuning off the grid"
I have included a python notebook that provides implementations of grid-search, random-search and Bayesian Optimization on a simple IMDB movie review classification task.
The IMDB data is availible from: https://github.com/jalbertbowden/large-movie-reviews-dataset/tree/master/acl-imdb-v1 and needs to be put in a folder named Data.
To learn more about using BO for tuning machine learning model I reccomend the following tutorials: http://nbviewer.jupyter.org/github/SheffieldML/GPyOpt/blob/master/manual/index.ipynb
Sklearn provides clear introductions for grid and random search: http://scikit-learn.org/stable/modules/grid_search.html