aangelopoulos/ppi_py
A package for statistically rigorous scientific discovery using machine learning. Implements prediction-powered inference.
This tool helps scientists and researchers make more accurate and reliable conclusions when using machine learning models to analyze data. It takes a small set of meticulously labeled "gold-standard" data, combined with a larger set of unlabeled data and model predictions, to produce better estimates and tighter confidence intervals for population statistics. The end-user is typically a domain expert or scientist who applies machine learning in fields like biology, astronomy, or social science, and needs statistically sound results.
279 stars.
Use this if you need to derive statistically rigorous insights and confidence intervals from your data, especially when you have limited gold-standard labels but abundant predictions from a machine learning model.
Not ideal if your primary goal is only model development or improving prediction accuracy, rather than making statistical inferences about a population.
Stars
279
Forks
34
Language
Python
License
MIT
Last pushed
Feb 24, 2026
Commits (30d)
0
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