Hyperparameter search space pruning – A new component for sequential model-based hyperparameter optimization

Abstract

The optimization of hyperparameters is often done manually or exhaustively but recent work has shown that automatic methods can optimize hyperparameters faster and even achieve better final performance. Sequential model-based optimization (SMBO) is the current state of the art framework for automatic hyperparameter optimization. Currently, it consists of three components: a surrogate model, an acquisition function and an initialization technique. We propose to add a fourth component, a way of pruning the hyperparameter search space which is a common way of accelerating the search in many domains but yet has not been applied to hyperparameter optimization. We propose to discard regions of the search space that are unlikely to contain better hyperparameter configurations by transferring knowledge from past experiments on other data sets as well as taking into account the evaluations already done on the current data set. Pruning as a new component for SMBO is an orthogonal contribution but nevertheless we compare it to surrogate models that learn across data sets and extensively investigate the impact of pruning with and without initialization for various state of the art surrogate models. The experiments are conducted on two newly created meta-data sets which we make publicly available. One of these meta-data sets is created on 59 data sets using 19 different classifiers resulting in a total of about 1.3 million experiments. This is by more than four times larger than all the results collaboratively collected by OpenML.