首页|Studies from University of Maryland Update Current Data on Machine Learning (Ben chmarking Automl Solutions for Concrete Strength Prediction: Reliability, Uncert ainty, and Dilemma)
Studies from University of Maryland Update Current Data on Machine Learning (Ben chmarking Automl Solutions for Concrete Strength Prediction: Reliability, Uncert ainty, and Dilemma)
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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."
College ParkMarylandUnited StatesN orth and Central AmericaCyborgsEmerging TechnologiesMachine LearningUniv ersity of Maryland
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