  During the training phase of machine learning (ML) models, it is usually
necessary to configure several hyperparameters. This process is computationally
intensive and requires an extensive search to infer the best hyperparameter set
for the given problem. The challenge is exacerbated by the fact that most ML
models are complex internally, and training involves trial-and-error processes
that could remarkably affect the predictive result. Moreover, each
hyperparameter of an ML algorithm is potentially intertwined with the others,
and changing it might result in unforeseeable impacts on the remaining
hyperparameters. Evolutionary optimization is a promising method to try and
address those issues. According to this method, performant models are stored,
while the remainder are improved through crossover and mutation processes
inspired by genetic algorithms. We present VisEvol, a visual analytics tool
that supports interactive exploration of hyperparameters and intervention in
this evolutionary procedure. In summary, our proposed tool helps the user to
generate new models through evolution and eventually explore powerful
hyperparameter combinations in diverse regions of the extensive hyperparameter
space. The outcome is a voting ensemble (with equal rights) that boosts the
final predictive performance. The utility and applicability of VisEvol are
demonstrated with two use cases and interviews with ML experts who evaluated
the effectiveness of the tool.
