acmi-lab/PU_learning
Code and results accompanying our paper titled Mixture Proportion Estimation and PU Learning: A Modern Approach at Neurips 2021 (Spotlight)
This project helps researchers and data scientists classify data when they only have positive examples and a mix of positive and unlabeled examples. It takes your dataset of labeled positive instances and unlabeled instances as input and outputs a model that can accurately predict new positive cases. This is for machine learning researchers and data scientists working on classification problems with incomplete label information.
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Use this if you need to build a classifier but only have a small set of confirmed positive examples and a large set of data where you don't know if they are positive or negative.
Not ideal if you have a dataset with clear positive and negative labels, as standard supervised classification methods would be more appropriate.
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Language
Python
License
Apache-2.0
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Last pushed
Mar 12, 2024
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