VisEvol: Visual Analytics to Support Hyperparameter Search through Evolutionary Optimization
This tool aims to simplify and improve the computationally intensive and complex process of hyperparameter tuning for machine learning practitioners, particularly when dealing with intertwined hyperparameters.
This paper introduces VisEvol, a visual analytics tool designed to assist in hyperparameter optimization for machine learning models using evolutionary algorithms. The tool enables interactive exploration and intervention in the evolutionary process, leading to the generation of new models and the discovery of powerful hyperparameter combinations, ultimately boosting predictive performance through a voting ensemble.
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.