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English(EN) Robust XGBoosting for Regression

XGBoost增强以提高预测和鲁棒性 · 2篇论文

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新研究论文提出了对XGBoost算法的增强，以改进回归和预测。第一篇论文介绍了QXGB，一个基于分位数的梯度提升框架，它使用卷积平滑损失来更好地估计条件分位数并表征极端结果，并在PM2.5预测任务中得到证明。第二篇论文通过探索替代损失函数来解决XGBoost对异常值的鲁棒性问题，提出了MM-XGBoost作为一种平衡鲁棒性和预测准确性的两步程序。 AI

影响 这些研究论文在预测建模和机器学习算法鲁棒性方面提供了进展，有可能提高各种应用的准确性和可靠性。

排序理由 两篇在arXiv上发表的学术论文，提出了XGBoost算法的新颖方法。

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

XGBoost增强以提高预测和鲁棒性 · 2篇论文

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两篇在arXiv上发表的学术论文，提出了XGBoost算法的新颖方法。
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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Mandy Yao (University of Toronto), Meredith Franklin (University of Toronto) ·

    XGBoost 的卷积平滑分位数回归

    arXiv:2608.15290v1 Announce Type: new Abstract: The increasing availability of large and complex datasets across many scientific disciplines has led to widespread adoption of machine learning (ML) for prediction. However, most ML algorithms focus on point estimation and provide l…

  2. arXiv stat.ML TIER_1 English(EN) · Iris Arag\'on Mladosich, Christophe Croux ·

    用于回归的鲁棒XGBoosting

    arXiv:2608.13590v1 Announce Type: cross Abstract: XGBoost is a very popular and powerful method for prediction. It iteratively fits simple decision trees to the residuals of the previous step. An efficient and scalable implementation is available. The standard loss function for X…

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