mlr-org/mlr3fselect
Feature selection package of the mlr3 ecosystem.
This tool helps data scientists and machine learning engineers build more accurate and efficient predictive models. It takes your raw dataset with many potential input variables and a chosen machine learning model, then intelligently identifies the most relevant subset of variables. The output is a refined list of crucial input variables and a well-performing model trained on this optimized selection.
Use this if you are working on a classification or regression problem and suspect your dataset has too many input variables, some of which might be redundant or irrelevant, leading to lower model performance or slower training.
Not ideal if you are a business user simply looking to apply an existing model or if you need to interpret the contribution of every single variable in your dataset.
Stars
38
Forks
5
Language
R
License
LGPL-3.0
Category
Last pushed
Mar 19, 2026
Commits (30d)
0
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