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data quality

topic1 events
papersTODAY 04:00 UTC

arXiv Paper Reviews Machine Learning Methods for Imperfect Training Data

A new arXiv preprint examines how machine-learning pipelines behave when training or test data is incomplete, imbalanced, poorly labelled, or drawn from mismatched distributions. The authors survey measurement approaches and methods designed to keep models reliable under these common real-world conditions. The work is framed as an overview of challenges and remedies rather than a new model release.

arxiv/machine-learning/data-quality/research