Old-school classifiers are still generally better than Jev [P]
A discussion on reddit.com suggests that traditional classifiers often outperform newer decision models like Jev, particularly when labeled training data is available. The advantage stems from the ability to personalize models using this data, a feature Jev lacks. A write-up on blog.voyageai.com and code on github.com/mishelsikder/mongo-predict illustrate this approach, emphasizing that the benefits are contingent on the presence of training labels.
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发布当时偏移：UTC+02026年10月29日 14:13 UTC
收录当时偏移：UTC+02026年10月29日 19:00 UTC
- 发布
- 2026年10月29日 14:13
- 收录
- 2026年10月29日 19:00
- 来源类型
- 开发者社区
- 档位
- 社区
- 信源状态
- 正常
档位是按信源手工设定的编辑判断，不是逐条打分。
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.