UrbsLab/STREAMLINE

Simple Transparent End-To-End Automated Machine Learning Pipeline for Supervised Learning in Tabular Binary Classification Data

51
/ 100
Established

This tool helps scientists and researchers automatically build and interpret predictive models from their tabular data. You input a spreadsheet or similar dataset where rows are observations and columns are features, along with a column indicating a 'yes/no' or 'positive/negative' outcome. It then provides transparent insights into which factors predict that outcome, along with a ready-to-use predictive model.

Use this if you need to understand what factors influence a binary outcome in your tabular data (like 'disease present' vs. 'disease absent') and want an automated, interpretable predictive model.

Not ideal if your data includes text, images, or time-series information, or if you need to predict a numerical value or more than two categories.

biomedical-research clinical-prediction scientific-modeling data-mining risk-assessment
No Package No Dependents
Maintenance 10 / 25
Adoption 9 / 25
Maturity 16 / 25
Community 16 / 25

How are scores calculated?

Stars

80

Forks

12

Language

Jupyter Notebook

License

GPL-3.0

Last pushed

Mar 11, 2026

Commits (30d)

0

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