AdrianBZG/TabMDA
[ICML 2024] TabMDA: Tabular Manifold Data Augmentation for Any Classifier using Transformers with In-context Subsetting
When you have a small amount of crucial tabular data, like customer demographics or medical records, machine learning models often struggle to find reliable patterns. This project helps by taking your limited tabular dataset and intelligently generating more synthetic, yet realistic, data points. The output is an expanded dataset that helps your existing classification models perform better, making it easier for data scientists to get accurate predictions from scarce information.
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Use this if you need to improve the performance of a machine learning classifier on a tabular dataset where data is scarce, and you want a method that doesn't require extra training.
Not ideal if you already have very large tabular datasets, as the performance gains might be less significant.
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Language
Python
License
MIT
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Last pushed
Jul 26, 2024
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