mlr-org/mlr3tuningspaces
Collection of search spaces for hyperparameter optimization in the mlr3 ecosystem
When building machine learning models, finding the best settings for your chosen algorithm can significantly improve its performance. This tool provides pre-defined, scientifically-backed configurations for popular algorithms like 'xgboost' or 'ranger'. It takes your chosen machine learning model and provides a set of recommended settings to try, helping data scientists and machine learning engineers achieve better model accuracy and efficiency.
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Use this if you are a data scientist or machine learning engineer working in R and want to quickly apply robust, published hyperparameter tuning settings to your models without having to research or define them from scratch.
Not ideal if you need to design completely custom hyperparameter search spaces from first principles or are not working within the mlr3 ecosystem in R.
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Language
R
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Last pushed
Aug 16, 2025
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