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

Reddit r/MachineLearning 10/29/26, 02:13 PM News

embeddings classification scikit-learn fine-tuning voyage-ai benchmarks

Summary

A Voyage AI practitioner argues that embedding inputs with a cheap embedding model and training a simple sklearn classifier on labeled data beats hype-driven zero-shot decision models like Jev, reporting a 37% error reduction at lower cost (with the caveat that this advantage only applies when training labels are available).

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.

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