sibirbil/marsopt
Mixed Adaptive Random Search (MARS) for Optimization
This tool helps machine learning engineers and data scientists find the best settings for their models or algorithms. You provide a function that evaluates a set of parameters, and it efficiently explores different combinations to pinpoint the optimal ones. This means you get a set of ideal hyperparameters for your model or algorithm.
No commits in the last 6 months. Available on PyPI.
Use this if you need to automatically fine-tune machine learning model hyperparameters or optimize any complex 'black-box' function with a mix of continuous, integer, and categorical inputs.
Not ideal if your optimization problem can be solved with simple grid search or random search due to a very small search space, or if you require highly specific algorithmic control beyond adaptive random search.
Stars
30
Forks
2
Language
Python
License
MIT
Category
Last pushed
Oct 04, 2025
Commits (30d)
0
Dependencies
1
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/sibirbil/marsopt"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
optuna/optuna
A hyperparameter optimization framework
keras-team/keras-tuner
A Hyperparameter Tuning Library for Keras
KernelTuner/kernel_tuner
Kernel Tuner
syne-tune/syne-tune
Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.
deephyper/deephyper
DeepHyper: A Python Package for Massively Parallel Hyperparameter Optimization in Machine Learning