Yu-Group/veridical-flow
Making it easier to build stable, trustworthy data-science pipelines based on the PCS framework.
This helps data scientists and machine learning engineers build more reliable and stable predictive models. You input your raw data and pipeline steps (like preprocessing, modeling, and evaluation), and it outputs an assessment of how sensitive your final results are to different choices made along the way. This tool is for anyone developing or deploying data science solutions who needs to ensure their models are robust and trustworthy.
No commits in the last 6 months.
Use this if you need to understand how variations in your data science pipeline, such as different data cleaning methods or model architectures, impact the stability and trustworthiness of your final predictions or insights.
Not ideal if you are looking for a simple tool to deploy a single, already-validated model without needing to explore the robustness of your pipeline's judgment calls.
Stars
72
Forks
8
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Feb 12, 2024
Commits (30d)
0
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