cgnorthcutt/rankpruning
🧹 Formerly for binary classification with noisy labels. Replaced by cleanlab.
This project offers a method for improving the accuracy of binary classification models when your training data has errors in the labels. It takes your existing data (features) and potentially mislabeled training outcomes, and outputs a more reliable model for predicting future outcomes. A research scientist or machine learning practitioner working with noisy datasets would find this useful.
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Use this if you are a research scientist trying to reproduce the results of the 2017 UAI publication, 'Learning with Confident Examples: Rank Pruning for Binary Classification with Noisy Labels'.
Not ideal if you are looking for a general-purpose solution for noisy labels; the `cleanlab` package is a more robust and updated alternative for broader applications.
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MIT
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Last pushed
May 15, 2022
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