label-efficiency

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rajul-kk / varwise-label-efficiency

Active-learning label-efficiency study on the VarWISE NEOWISE infrared variable catalog: 86% fewer labels for equal accuracy, with the gain concentrated almost entirely in the rarest variability classes. Includes an independent SIMBAD validation of VarWISE's published classifications.

astronomy xgboost lightgbm class-imbalance active-learning variable-stars astroml label-efficiency varwise

Updated Aug 18, 2026
Python

tkarim45 / active-learning

Star

Label-efficiency benchmark — margin sampling hits target accuracy with 38% fewer labels than random, while least-confidence/entropy underperform it. The honest 'which strategy, not whether'

python machine-learning annotation scikit-learn ml active-learning uncertainty-sampling sample-selection data-centric-ai label-efficiency

Updated Jul 25, 2026
Python

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