Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance metrics do not capture it. Consequently, a model might offer sufficient ...

Machine learning models delivered the strongest performance across nearly all evaluation metrics. CHAID and CART provided the highest and most stable sensitivity, accuracy and discriminatory power, ...

Overview: Machine learning failures usually start before modeling, with poor data understanding and preparation.Clean data, ...

Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...

Delphi lets users watch machine learning models compete live on benchmarks and buy a stake in those the user thinks are best, ...

Machine learning is transforming many scientific fields, including computational materials science. For about two decades, ...

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting? Based on our evaluation of all models from both classes ever used in ...

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...

Software engineers are increasingly seeking structured pathways to transition into machine learning roles as companies expand ...