  Machine learning (ML) methods are used in most technical areas such as image
recognition, product recommendation, financial analysis, medical diagnosis, and
predictive maintenance. An important aspect of implementing ML methods involves
controlling the learning process for the ML method so as to maximize the
performance of the method under consideration. Hyperparameter tuning is the
process of selecting a suitable set of ML method parameters that control its
learning process. In this work, we demonstrate the use of discrete simulation
optimization methods such as ranking and selection (R&S) and random search for
identifying a hyperparameter set that maximizes the performance of a ML method.
Specifically, we use the KN R&S method and the stochastic ruler random search
method and one of its variations for this purpose. We also construct the
theoretical basis for applying the KN method, which determines the optimal
solution with a statistical guarantee via solution space enumeration. In
comparison, the stochastic ruler method asymptotically converges to global
optima and incurs smaller computational overheads. We demonstrate the
application of these methods to a wide variety of machine learning models,
including deep neural network models used for time series prediction and image
classification. We benchmark our application of these methods with
state-of-the-art hyperparameter optimization libraries such as $hyperopt$ and
$mango$. The KN method consistently outperforms $hyperopt$'s random search (RS)
and Tree of Parzen Estimators (TPE) methods. The stochastic ruler method
outperforms the $hyperopt$ RS method and offers statistically comparable
performance with respect to $hyperopt$'s TPE method and the $mango$ algorithm.
