首页|Researchers at Stanford University Release New Study Findings on Machine Learnin g (Practical guide to building machine learningbased clinical prediction models using imbalanced datasets)
Researchers at Stanford University Release New Study Findings on Machine Learnin g (Practical guide to building machine learningbased clinical prediction models using imbalanced datasets)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intell igence have been published. According to news originating from Stanford, Califor nia, by NewsRx correspondents, research stated, “Clinical prediction models ofte n aim to predict rare, high-risk events, but building such models requires robus t understanding of imbalance datasets and their unique study design consideratio ns.” Our news journalists obtained a quote from the research from Stanford University : “This practical guide highlights foundational prediction model principles for surgeon-data scientists and readers who encounter clinical prediction models, fr om feature engineering and algorithm selection strategies to model evaluation an d design techniques specific to imbalanced datasets. We walk through a clinical example using readable code to highlight important considerations and common pit falls in developing machine learning-based prediction models.”
Stanford UniversityStanfordCaliforni aUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMac hine LearningSurgery
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