glmnet is a widely adopted R package for lasso estimation due to its computational efficiency. Despite its popularity, glmnet sometimes yields solutions that are substantially different from the true ones because of the inappropriate default configuration of the algorithm. The accuracy of the obtained solutions can be improved by appropriately tuning the configuration. However, improving accuracy typically increases computational time, resulting in a trade-off between accuracy and computational efficiency. Therefore, it is essential to establish a systematic approach to determine appropriate configuration. To address this need, we propose a unified data-driven framework specifically designed to optimize the configuration by balancing the trade-off between accuracy and computational efficiency. We generate large-scale simulated datasets and apply glmnet under various configurations to obtain accuracy and computation time. Based on these results, we construct neural networks that predict accuracy and computation time from data characteristics and configuration. Given a new dataset, our framework uses the neural networks to explore the configuration space and derive a Pareto front that represents the trade-off between accuracy and computational cost. This front allows us to automatically identify the configuration that maximize accuracy under a user-specified time constraint. The proposed method is implemented in the R package 'glmnetconf', available at https://github.com/Shuhei-Muroya/glmnetconf.

翻译：glmnet因其计算效率而成为广泛采用的lasso估计R包。尽管广受欢迎，但由于算法默认配置不当，glmnet有时会产生与真实解存在显著差异的结果。通过适当调整配置可以提高所得解的精度，但精度提升通常会增加计算时间，导致精度与计算效率之间的权衡。因此，建立系统化的方法来确定合适的配置至关重要。为满足这一需求，我们提出了一个统一的数据驱动框架，专门用于通过平衡精度与计算效率之间的权衡来优化配置。我们生成大规模模拟数据集，在不同配置下应用glmnet以获取精度和计算时间。基于这些结果，我们构建了从数据特征和配置预测精度与计算时间的神经网络。对于新数据集，我们的框架利用神经网络探索配置空间，并生成表示精度与计算成本之间权衡的帕累托前沿。该前沿使我们能够在用户指定的时间约束下自动识别最大化精度的配置。所提方法已实现在R包'glmnetconf'中，可通过https://github.com/Shuhei-Muroya/glmnetconf获取。