VisEvol: Visual Analytics to Support Hyperparameter Search through Evolutionary Optimization

arXiv:2012.01205v4 Announce Type: replace
Abstract: 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 …

abstract analytics arxiv challenge cs.hc cs.lg hyperparameter machine machine learning optimization process search set stat.ml support through training type visual visual analytics