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English(EN) LLM Classification Is Feature Engineering https://minimallysufficient.com/posts/llm-classification-is-feature-extraction/ # HackerNews # Tech # AI

LLM 作为分类的特征工程工具

本文认为,使用大型语言模型(LLM)进行分类任务本质上是一种特征工程。与需要手动创建特征的传统方法不同,LLM 可以自动从文本数据中提取相关特征。这种方法简化了过程,并可能带来更有效的分类模型。 AI

影响 将 LLM 在分类中的应用重新定义为一种复杂的特征工程形式,可能影响模型开发策略。

排序理由 该集群讨论的是 LLM 用法的概念框架,而不是新的发布或事件。

在 Mastodon — sigmoid.social 阅读 →

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

LLM 作为分类的特征工程工具

本文如何被排名

Signal score
3 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Commentary
该集群讨论的是 LLM 用法的概念框架,而不是新的发布或事件。
Source corroboration
2 independent sources
Multiple independent publishers reporting the same story raises confidence that it's real and newsworthy.
Topics
paper, 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
Breaking (< 6h)
Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准

报道来源 [2]

  1. Mastodon — sigmoid.social TIER_1 English(EN) · [email protected] ·

    LLM 分类即特征工程 https://minimallysufficient.com/posts/llm-classification-is-feature-extraction/ # HackerNews # Tech # AI

    LLM Classification Is Feature Engineering https://minimallysufficient.com/posts/llm-classification-is-feature-extraction/ # HackerNews # Tech # AI

  2. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    LLM 分类即特征工程 https:// minimallysufficient.com/posts/ llm-classification-is-feature-extraction/ Comments: https:// news.ycombinator.

    LLM Classification Is Feature Engineering https:// minimallysufficient.com/posts/ llm-classification-is-feature-extraction/ Comments: https:// news.ycombinator.com/item?id=4 9742437 # HackerNews # LLM # Classification # FeatureEngineering # MachineLearning # DataScience # AI