zygmuntz/hyperband
Tuning hyperparams fast with Hyperband
This tool helps data scientists and machine learning practitioners quickly find the best settings for their predictive models, such as gradient boosting or random forests. You provide your dataset and the tool efficiently explores different model configurations. The output is the most effective set of model parameters and their performance metrics.
596 stars. No commits in the last 6 months.
Use this if you need to optimize the performance of your machine learning models by efficiently searching for the best hyperparameters.
Not ideal if you're not working with scikit-learn compatible classification or regression models, or if you prefer a graphical user interface for model tuning.
Stars
596
Forks
73
Language
Python
License
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Category
Last pushed
Aug 15, 2018
Commits (30d)
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