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.

翻译：在机器学习模型的训练阶段，通常需要配置多项超参数。该过程计算密集且需要进行广泛搜索，以推断给定问题的最佳超参数集。由于大多数机器学习模型内部结构复杂，训练涉及试错过程，这些过程可能显著影响预测结果，从而加剧了挑战。此外，机器学习算法的各项超参数之间可能相互关联，改变其中一项可能对其他超参数产生不可预见的影响。进化优化是尝试解决上述问题的一种有前景的方法。该方法将表现优异的模型保留下来，其余模型则通过受遗传算法启发的交叉与变异过程进行改进。我们提出了VisEvol——一种支持超参数交互式探索及对该进化过程进行干预的可视化分析工具。总之，本文提出的工具通过进化生成新模型，帮助用户在广阔超参数空间的不同区域探索强大的超参数组合，最终形成具有平等投票权的投票集成模型，以提升最终预测性能。通过两个使用案例以及评估该工具有效性的机器学习专家访谈，验证了VisEvol的实用性与适用性。