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Automatically Debugging AutoML Pipelines Using Maro: ML Automated Remediation Oracle (Extended Version)
[article]
2022
arXiv
pre-print
Machine learning in practice often involves complex pipelines for data cleansing, feature engineering, preprocessing, and prediction. These pipelines are composed of operators, which have to be correctly connected and whose hyperparameters must be correctly configured. Unfortunately, it is quite common for certain combinations of datasets, operators, or hyperparameters to cause failures. Diagnosing and fixing those failures is tedious and error-prone and can seriously derail a data scientist's
arXiv:2205.01311v1
fatcat:xvlk2rmtlnbvbo2gggvxz7tbzy