Data scientists are constantly facing the problem of how to improve prediction accuracy with insufficient tabular data. We propose a table enrichment system that enriches a query table by adding external attributes (columns) from data lakes and improves the accuracy of machine learning predictive models. Our system has four stages, join row search, task-related table selection, row and column alignment, and feature selection and evaluation, to efficiently create an enriched table for a given query table and a specified machine learning task. We demonstrate our system with a web UI to show the use cases of table enrichment.
@article{arxiv.2204.08235,
title = {Table Enrichment System for Machine Learning},
author = {Yuyang Dong and Masafumi Oyamada},
journal= {arXiv preprint arXiv:2204.08235},
year = {2022}
}