An uncalibrated classifier is worse than no classifier
What I learned building a website that rewrites itself for whoever is reading it, on ₹0 of infrastructure. Project Blog : https://santhosh-reddy.vercel.app/en/blog/8 Project Breakdown : https://santhosh-reddy.vercel.app/en/project/8 My portfolio gets very different readers. A recruiter clicking through from a LinkedIn job post wants to know whether I'm hireable for an internship. A developer…
A recent project demonstrated that an uncalibrated classifier performs worse than having no classifier at all. The project, called Evolve, used a free, Jev-style typed-decision API to route visitors with varying confidence levels. In some cases, this resulted in conversion rates lower than those achieved with no segmentation at all.
The key issue was that a poorly calibrated classifier split traffic into populations that learned from the wrong influences, leading to slower convergence and wrong outcomes. The solution was calibration rather than improving raw accuracy. To create the classifier, the project used a seeded generative model trained on honest targets, and then fine-tuned a MiniLM-L6 student model on Oracle's free micro VM.
The resulting model was able to sustain about 8 fresh decisions per second, with limited RAM and memory requirements, while achieving good performance on unseen referrer hosts.
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