  To mitigate the pain of manually tuning hyperparameters of deep neural
networks, automated machine learning (AutoML) methods have been developed to
search for an optimal set of hyperparameters in large combinatorial search
spaces. However, the search results of AutoML methods significantly depend on
initial configurations, making it a non-trivial task to find a proper
configuration. Therefore, human intervention via a visual analytic approach
bears huge potential in this task. In response, we propose HyperTendril, a
web-based visual analytics system that supports user-driven hyperparameter
tuning processes in a model-agnostic environment. HyperTendril takes a novel
approach to effectively steering hyperparameter optimization through an
iterative, interactive tuning procedure that allows users to refine the search
spaces and the configuration of the AutoML method based on their own insights
from given results. Using HyperTendril, users can obtain insights into the
complex behaviors of various hyperparameter search algorithms and diagnose
their configurations. In addition, HyperTendril supports variable importance
analysis to help the users refine their search spaces based on the analysis of
relative importance of different hyperparameters and their interaction effects.
We present the evaluation demonstrating how HyperTendril helps users steer
their tuning processes via a longitudinal user study based on the analysis of
interaction logs and in-depth interviews while we deploy our system in a
professional industrial environment.
