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Studies from Royal Melbourne Institute of Technology - RMIT University Further Understanding of Machine Learning (A Monte Carlo Fuzzy Logistic Regression Framework Against Imbalance and Separation)

机译:皇家墨尔本理工学院皇家墨尔本理工大学的研究:进一步了解机器学习(蒙特卡洛模糊逻辑回归框架,对抗不平衡和分离)

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By a News Reporter-Staff News Editor at Robotics Machine Learning DailyNews Daily News – Investigators publish new report on Machine Learning. According to news originatingfrom Melbourne, Australia, by NewsRx correspondents, research stated, “This article proposes a new fuzzylogistic regression framework with high classification performance against imbalance and separation whilekeeping the interpretability of classical logistic regression. Separation and imbalance are two core problemsin logistic regression, which can result in biased coefficient estimates and inaccurate predictions.”
机译:作者:机器人与机器学习日报新闻新闻编辑 - 调查人员发布了关于机器学习的新报告。据NewsRx记者发自澳大利亚墨尔本的新闻报道,研究指出,“本文提出了一种新的模糊逻辑回归框架,该框架在保持经典逻辑回归的可解释性的同时,具有针对不平衡和分离的高分类性能。分离和不平衡是逻辑回归中的两个核心问题,这可能导致偏差的系数估计和不准确的预测。

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