smazzanti/mrmr
mRMR (minimum-Redundancy-Maximum-Relevance) for automatic feature selection at scale.
When building predictive models, you often have many data points (features) but only a few are truly useful. This tool helps data scientists and machine learning engineers automatically identify the smallest set of the most important features from large datasets. It takes your raw dataset and a target variable, then outputs a ranked list of the most relevant and least redundant features for your predictive task.
623 stars. No commits in the last 6 months.
Use this if you need to quickly and automatically identify a minimal yet highly effective set of features for a machine learning model, especially when dealing with large datasets or needing faster model training and better explainability.
Not ideal if your primary goal is to identify every single feature that has any relationship with your target variable, rather than focusing on a minimal, optimal set.
Stars
623
Forks
90
Language
Python
License
MIT
Category
Last pushed
Nov 19, 2024
Commits (30d)
0
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