Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular Data

ArXi:2310.02540v2 Announce Type: replace-cross Classical machine learning models, such as linear models and tree-based models, are widely used in industry. These models are sensitive to data distribution, thus feature preprocessing, which transforms features from one distribution to another, is a crucial step to ensure good model quality. Manually constructing a feature preprocessing pipeline is challenging because data scientists need to make difficult decisions about which preprocessors to select and in which order to compose them.