robertvacareanu/llm4regression
Examining how large language models (LLMs) perform across various synthetic regression tasks when given (input, output) examples in their context, without any parameter update
This project helps data scientists and analysts make predictions by using large language models (LLMs) for regression tasks. You provide the LLM with example input-output pairs, and it learns to predict numerical outcomes without needing complex traditional machine learning models. The output is a numerical prediction for new inputs, offering a potentially simpler approach to forecasting.
162 stars. No commits in the last 6 months.
Use this if you need to predict numerical values from given inputs and want to explore using readily available LLMs instead of traditional regression models.
Not ideal if you require deep insights into model coefficients, feature importance, or the mathematical underpinnings of a traditional regression model.
Stars
162
Forks
20
Language
Python
License
—
Category
Last pushed
Oct 12, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/robertvacareanu/llm4regression"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
NX-AI/xlstm
Official repository of the xLSTM.
sinanuozdemir/oreilly-hands-on-gpt-llm
Mastering the Art of Scalable and Efficient AI Model Deployment
DashyDashOrg/pandas-llm
Pandas-LLM
wxhcore/bumblecore
An LLM training framework built from the ground up, featuring a custom BumbleBee architecture...
MiniMax-AI/MiniMax-01
The official repo of MiniMax-Text-01 and MiniMax-VL-01, large-language-model &...