YujiaBao/Predict-then-Interpolate

"Predict, then Interpolate: A Simple Algorithm to Learn Stable Classifiers" ICML 2021

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Experimental

This project helps machine learning researchers and practitioners build more reliable classification models. It takes your existing labeled datasets from different training environments and produces a robust classifier that performs well even when the underlying data patterns shift slightly. This is useful for anyone deploying models in real-world scenarios where data characteristics might change over time or across different sources.

No commits in the last 6 months.

Use this if you need to build a classification model that remains accurate and stable across varied data conditions or different populations.

Not ideal if you only have a single training dataset and are not concerned with model stability across different environments.

predictive-modeling machine-learning-research model-robustness data-shift classification-stability
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 16 / 25
Community 5 / 25

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Stars

18

Forks

1

Language

Python

License

MIT

Last pushed

Jun 01, 2021

Commits (30d)

0

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