YujiaBao/Predict-then-Interpolate
"Predict, then Interpolate: A Simple Algorithm to Learn Stable Classifiers" ICML 2021
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.
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Language
Python
License
MIT
Category
Last pushed
Jun 01, 2021
Commits (30d)
0
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