jsvir/lscae
Deep unsupervised feature selection by discarding nuisance and correlated features
This tool helps data scientists and machine learning engineers prepare datasets for modeling by automatically identifying and removing redundant or irrelevant features. It takes your raw, scaled numerical data and outputs a refined dataset containing only the most informative features, improving model performance and interpretability. This is for professionals who work with high-dimensional data and want to streamline their feature engineering process.
No commits in the last 6 months. Available on PyPI.
Use this if you are a data scientist dealing with large datasets and need an automated way to reduce dimensionality by selecting the most meaningful features without relying on labeled data.
Not ideal if you require a feature selection method that leverages labeled data or if you need to understand the causal relationships between features and your target variable.
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8
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4
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jul 19, 2023
Commits (30d)
0
Dependencies
5
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