smazzanti/mrmr
A Python library implementing the minimum-Redundancy-Maximum-Relevance (mRMR) algorithm for automated feature selection in machine learning workflows.
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mRMR provides minimal-optimal feature selection, finding the smallest relevant subset of features for a given ML task. It supports multiple backends including Pandas, Polars, Spark, and Google BigQuery for large-scale feature selection. The algorithm optimizes for both relevance to the target variable and redundancy between selected features, making it efficient for regular ML workflows where feature selection must be performed automatically and frequently.
Frequently asked
- What is smazzanti/mrmr?
- A Python library implementing the minimum-Redundancy-Maximum-Relevance (mRMR) algorithm for automated feature selection in machine learning workflows.
- Is mrmr open source?
- Yes — smazzanti/mrmr is open source, released under the MIT license.
- What language is mrmr written in?
- smazzanti/mrmr is primarily written in Python.
- How popular is mrmr?
- smazzanti/mrmr has 629 stars on GitHub.
- Where can I find mrmr?
- smazzanti/mrmr is on GitHub at https://github.com/smazzanti/mrmr.