Model-free feature selection to facilitate automatic discovery of divergent subgroups in tabular data. (arXiv:2203.04386v1 [cs.LG])

Data-centric AI encourages the need of cleaning and understanding of data in
order to achieve trustworthy AI. Existing technologies, such as AutoML, make it
easier to design and train models automatically, but there is a lack of a
similar level of capabilities to extract data-centric insights. Manual
stratification of tabular data per a feature (e.g., gender) is limited to scale
up for higher feature dimension, which could be addressed using automatic
discovery of divergent subgroups. Nonetheless, these automatic discovery
techniques …

arxiv data discovery feature selection subgroups tabular tabular data