phuijse/bagging_pu
Simple sklearn based python implementation of Positive-Unlabeled (PU) classification using bagging based ensembles
This tool helps you train a classification model when you only have examples of one class (positive examples) and a large pool of unlabeled data, some of which might also be positive. It takes your positive and unlabeled datasets and outputs a model that can predict whether new, unseen data points are positive or not. This is ideal for data scientists, machine learning engineers, and researchers working with incomplete datasets.
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Use this if you need to build a classifier but only have a small number of confirmed 'positive' examples and a large amount of data where the 'negative' examples are not explicitly labeled.
Not ideal if you have clearly labeled examples for both your positive and negative classes, as standard supervised classification methods would be more straightforward.
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93
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Language
Jupyter Notebook
License
MIT
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Last pushed
Jan 03, 2017
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