(AF) LLMs didn't kill feature engineering. Engineers did.

作者认为，尽管有大型语言模型，特征工程仍然至关重要

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-30 18:04

即使大型语言模型（LLMs）兴起，特征工程对于机器学习模型仍然至关重要。作者认为，输入模型的特征质量对准确性的影响远大于算法本身的选择。虽然大型语言模型可以自动化部分特征提取，特别是从非结构化文本中提取，但它们并不能取代深思熟虑的特征工程的必要性，尤其是在处理结构化业务数据时。 AI

影响认为大型语言模型并未消除手动特征工程的必要性，尤其是在结构化数据方面，这影响了AI模型的构建和部署方式。

排序理由该条目是一篇观点文章，在大型语言模型的背景下，就特征工程的持续相关性提出了特定论点。

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Commentary; 该条目是一篇观点文章，在大型语言模型的背景下，就特征工程的持续相关性提出了特定论点。
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 99 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

dev.to — LLM tag TIER_1 (AF) · Jignesh Maheshwari · 2026-06-30 18:04

大型语言模型并未扼杀特征工程，而是工程师们做到了。

<p>Somewhere around when LLMs started eating every roadmap, a quiet belief took over a lot of teams. If the model is big enough, it'll figure out the patterns on its own. Why bother hand crafting features when you can just throw raw data at something with a few hundred billion pa…