  Novel technologies in automated machine learning ease the complexity of
algorithm selection and hyperparameter optimization. Hyperparameters are
important for machine learning models as they significantly influence the
performance of machine learning models. Many optimization techniques have
achieved notable success in hyperparameter tuning and surpassed the performance
of human experts. However, depending on such techniques as blackbox algorithms
can leave machine learning practitioners without insight into the relative
importance of different hyperparameters. In this paper, we consider building
the relationship between the performance of the machine learning models and
their hyperparameters to discover the trend and gain insights, with empirical
results based on six classifiers and 200 datasets. Our results enable users to
decide whether it is worth conducting a possibly time-consuming tuning
strategy, to focus on the most important hyperparameters, and to choose
adequate hyperparameter spaces for tuning. The results of our experiments show
that gradient boosting and Adaboost outperform other classifiers across 200
problems. However, they need tuning to boost their performance. Overall, the
results obtained from this study provide a quantitative basis to focus efforts
toward guided automated hyperparameter optimization and contribute toward the
development of better-automated machine learning frameworks.
