llm_optimize
evalml
| llm_optimize | evalml | |
|---|---|---|
| 3 | 2 | |
| 43 | 713 | |
| - | 1.1% | |
| 5.8 | 8.7 | |
| about 1 year ago | 3 days ago | |
| Python | Python | |
| MIT License | BSD 3-clause "New" or "Revised" License |
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llm_optimize
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Using Large Language Models for Hyperparameter Optimization, Zhang et al. 2023 [GPT-4 is quite good at finding the optimal hyperparameters for machine learning tasks]
Wrote this library as a way of doing this pretty plug and play: https://github.com/sshh12/llm_optimize
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Large Language Models as Optimizers
For anyone interested I've been working a very similar idea of LLMs for blackbox optimization: https://github.com/sshh12/llm_optimize
- Show HN: LLM Optimize, blackbox string optimization and AutoML with GPT-4
evalml
What are some alternatives?
LightAutoML - LAMA - automatic model creation framework
Sklearn-genetic-opt - ML hyperparameters tuning and features selection, using evolutionary algorithms.
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
easyopt - zero-code hyperparameters optimization framework
dify - Dify is an open-source LLM app development platform. Dify's intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features and more, letting you quickly go from prototype to production.
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
SAP-HANA-AutoML - Python Automated Machine Learning library for tabular data.
Auto_ViML - Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
powershap - A power-full Shapley feature selection method.
aps2020 - Code for the paper 'Variable Selection with Copula Entropy' published on Chinese Journal of Applied Probability and Statistics
FeatureHub - The most comprehensive library of AI/ML features across multiple domains. Our goal is to create a dataset that serves as a valuable resource for researchers and data scientists worldwide