changkun/bo
🔍 Bayesian Optimization in Go
This tool helps you quickly find optimal settings or configurations for a process based on your preferences or specific goals, especially when testing each option is time-consuming or expensive. You provide the potential options and feedback on their performance, and it suggests the next best option to try, ultimately guiding you to the best solution. It's designed for anyone who needs to fine-tune parameters in experiments, product design, or operational settings.
No commits in the last 6 months.
Use this if you need to systematically explore a range of possibilities and efficiently discover the best performing configuration without having to test every single option.
Not ideal if your problem involves evaluating only a few options, or if you can easily test every possible combination without significant cost or time.
Stars
9
Forks
2
Language
Go
License
MIT
Category
Last pushed
Jul 30, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/changkun/bo"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
SimonBlanke/Gradient-Free-Optimizers
Lightweight optimization with local, global, population-based and sequential techniques across...
Gurobi/gurobi-machinelearning
Formulate trained predictors in Gurobi models
emdgroup/baybe
Bayesian Optimization and Design of Experiments
heal-research/pyoperon
Python bindings and scikit-learn interface for the Operon library for symbolic regression.
simon-hirsch/ondil
A package for online distributional learning.