摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-调查人员发布了关于马学习的新报告。根据News Rx记者在马里兰州College Park的新闻报道，研究表明：“建立精确的机器学习和深度学习模型传统上需要数学技能和实践经验的结合，才能仔细调整超参数，从而显著地影响学习过程。随着数据集不断扩展到不同的引擎领域，"研究人员越来越多地转向机器学习方法来发现经典回归技术可能无法隐藏的洞察力."新闻记者引用了马里兰大学的一句话:“这种采用的激增引起了人们对结果蚂蚁元模型的充分性和对结果的解释的担忧。为了应对这些挑战,自动机器学习(AutoML)成为了一个有希望的解决方案。”本文旨在建立机器学习模型,在人类专家最少的干预或指导下,通过概述AutoML解决方案的原理,并将其应用于不同混凝土数据集中最重要的力学性能,即抗压强度的预测,利用来自不同混凝土类型、样本量和特征的9个数据集,详细讨论了高性能混凝土的基准数据集。将最佳实践应用于其他八个数据集。对于每种情况，除了集成和堆叠模式LS之外，还讨论了超参数调整的重要性。每个数据集采用基于树的模型来绘制SHAP图，相互之间的结果，并了解混合设计中每个成分对混凝土整体强度的贡献。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news reporting from College Park, Maryland, by News Rx journalists, research stated, "Building precise machine learning and deep lea rning models has traditionally required a combination of mathematical skills and hands-on experience to meticulously adjust hyperparameters that significantly i mpact the learning process. As datasets continue to expand across various engine ering domains, researchers increasingly turn to machine learning methods to unco ver hidden insights that may elude classic regression techniques." The news correspondents obtained a quote from the research from the University o f Maryland, "This surge in adoption raises concerns about the adequacy of result ant meta-models and the interpretation of findings. In response to these challen ges, automated machine learning (AutoML) emerges as a promising solution, aiming to construct machine learning models with minimal intervention or guidance from human experts. This paper benchmarks AutoML solutions by providing an overview of their principles and applying them to predict the most important mechanical p roperties of different concrete datasets, i.e., compressive strength. Nine datas ets from various concrete types, sample sizes, and features are utilized, with a detailed discussion on the benchmark dataset from high-performance concrete, ap plying best practices to the other eight datasets. For each case, the importance of hyperparameter tuning is discussed, alongside the ensemble and stacking mode ls. Tree-based models are employed for each dataset to develop SHAP plots, inter pret results, and understand the contribution of each component in the mix desig n to the overall strength of the concrete."
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