---
id: 20260317-T0-13
title: "研究破解表格机器学习'垃圾进垃圾出'悖论"
title_en: "Study Breaks 'Garbage In, Garbage Out' Paradox in Tabular ML"
url: https://ai.daily.yangsir.net/daily/20260317-T0-13
issue_date: 2026-03-17
publish_date: 2026-03-16T04:00:00.000Z
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2603.12288
---

# 研究破解表格机器学习'垃圾进垃圾出'悖论

arXiv论文提出数据架构理论，解释现代表格机器学习模型为何能使用高维、共线性、易出错的数据达到最先进性能。研究通过分析100个真实数据集，发现模型能自动识别并利用数据中的噪声特征，形成稳健预测机制。该理论为垃圾数据的高效利用提供了理论基础。

## English Version

**Study Breaks 'Garbage In, Garbage Out' Paradox in Tabular ML**

arXiv paper proposes data architecture theory explaining why modern tabular ML models excel with high-dimensional, noisy data. Analyzed 100 real datasets, finding models auto-detect and leverage noisy features for robust predictions, breaking traditional 'GI-GO' paradox.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2603.12288)

**详情页**：https://ai.daily.yangsir.net/daily/20260317-T0-13

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