mlr-org/mlr3hyperband
Successive Halving and Hyperband in the mlr3 ecosystem
This project helps data scientists efficiently find the best settings (hyperparameters) for their machine learning models. You provide your model and the range of settings to test, and it outputs the optimized settings that lead to better model performance. It's designed for data scientists and machine learning practitioners who build and refine predictive models.
Use this if you want to speed up the process of hyperparameter tuning for your machine learning models, especially with techniques like Successive Halving and Hyperband.
Not ideal if you are looking for a simple, manual way to adjust model parameters or if you are not working within the mlr3 R ecosystem.
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Language
R
License
LGPL-3.0
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Last pushed
Mar 19, 2026
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