用嵌入模型加sklearn分类器自建Jev式方案，误差比Jev低37%

Old-school classifiers are still generally better than Jev [P]

AI 导读

Voyage员工/u/csinva测试了用开源或廉价嵌入模型编码输入、再在自有标签上训练简单sklearn分类器的老派做法，结果显示该方案成本更低且几乎总是显著更准，相对Jev误差降低37%。作者说明这一优势来自拥有训练标签，而Jev不使用训练标签，因此在有标签的场景占优、无标签时则相反。相关写文和代码已公开。

正文

There’s a lot of hype right now around decision models like Jev. But in most real use cases, you have some labeled data, and can use it to personalize your model.

So we tested the old-school approach to classification and regression:

Embed your inputs with an open-source or cheap embedding model
Fit a simple sklearn model on top using your own labels

Turns out this approach was cheaper and almost always significantly more accurate (37% error reduction against Jev)

Write-up: https://blog.voyageai.com/2026/10/27/how-to-create-your-own-jev-using-voyage-models/ (Disclosure: I work at Voyage)
Code: https://github.com/mishelsikder/mongo-predict

Curious whether others have seen the same thing, or found cases where zero-shot performs better

Edit: To be clear, the gains here come from having training labels, which only applies to some use cases. Jev doesn’t use training labels, so it's unsuprising that this approaches wins when you have them and loses when you don't.

submitted by /u/csinva
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