In this game we challenge you to beat auto-sklearn, the winner of the recent ChaLearn AutoML challenge. With this little game we want to see if the human (you) can rival an automated procedure for supervised classification.
In a nutshell, auto-sklearn uses state-of-the-art Bayesian optimization to configure a flexible machine learning pipeline implemented in scikit-learn. Your job is to configure the machine learning pipeline with the form below.
Please find further instructions at the bottom of this page.
DO NOT CHANGE THE DOWNLOADED FILE. Re-upload it as it is.
Your configuration will be automatically evaluated with a time-limit of 5min. Although we carefully reduced the space of configurations, it is still possible to hit the time or memory limit. Such runs will be scored with -1. You can then check the log files for any error messages.
This website was built and tested with Firefox and does not work with Safari and other webkit-based browsers. To see the hyperparameter information you need to use a device with a mouse.
In this game you are tuning a simple machine learning pipeline with three steps:
All algorithms except xgradient_boosting are from the library scikit-learn and you can learn more about them in scikit-learn's documentation. The eXtreme Gradient Boosting algorithm comes from the package xgboost.