scikit-learn-contrib/fastcan
A fast canonical-correlation-based search algorithm for feature selection, system identification, data pruning, etc.
When building predictive models or analyzing data, you often have many potential inputs, but only a few are truly important. This tool helps you automatically identify the most relevant features or data points from your datasets, whether for predicting a single outcome or multiple outcomes. It's designed for data scientists, machine learning engineers, and researchers who need to simplify complex datasets and improve model efficiency.
Available on PyPI.
Use this if you need to quickly and efficiently select the most informative features or samples from large, potentially redundant datasets for machine learning or system identification.
Not ideal if you need a feature engineering tool to create new features, rather than select existing ones, or if your primary goal is model interpretability rather than raw predictive performance.
Stars
15
Forks
5
Language
Python
License
MIT
Category
Last pushed
Mar 06, 2026
Commits (30d)
0
Dependencies
1
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