  Most of the machine learning models have associated hyper-parameters along
with their parameters. While the algorithm gives the solution for parameters,
its utility for model performance is highly dependent on the choice of
hyperparameters. For a robust performance of a model, it is necessary to find
out the right hyper-parameter combination. Hyper-parameter optimization (HPO)
is a systematic process that helps in finding the right values for them. The
conventional methods for this purpose are grid search and random search and
both methods create issues in industrial-scale applications. Hence a set of
strategies have been recently proposed based on Bayesian optimization and
evolutionary algorithm principles that help in runtime issues in a production
environment and robust performance. In this paper, we compare the performance
of four python libraries, namely Optuna, Hyper-opt, Optunity, and sequential
model-based algorithm configuration (SMAC) that has been proposed for
hyper-parameter optimization. The performance of these tools is tested using
two benchmarks. The first one is to solve a combined algorithm selection and
hyper-parameter optimization (CASH) problem The second one is the NeurIPS
black-box optimization challenge in which a multilayer perception (MLP)
architecture has to be chosen from a set of related architecture constraints
and hyper-parameters. The benchmarking is done with six real-world datasets.
From the experiments, we found that Optuna has better performance for CASH
problem and HyperOpt for MLP problem.
