Data Budgeting for Machine Learning

• URL: http://arxiv.org/abs/2210.00987v1
• Date: Mon, 3 Oct 2022 14:53:17 GMT
• Title: Data Budgeting for Machine Learning
• Authors: Xinyi Zhao, Weixin Liang and James Zou
• Abstract summary: We study the data budgeting problem and formulate it as two sub-problems. We propose a learning method to solve data budgeting problems. Our empirical evaluation shows that it is possible to perform data budgeting given a small pilot study dataset with as few as $50$ data points.
• Score: 17.524791147624086
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data is the fuel powering AI and creates tremendous value for many domains. However, collecting datasets for AI is a time-consuming, expensive, and complicated endeavor. For practitioners, data investment remains to be a leap of faith in practice. In this work, we study the data budgeting problem and formulate it as two sub-problems: predicting (1) what is the saturating performance if given enough data, and (2) how many data points are needed to reach near the saturating performance. Different from traditional dataset-independent methods like PowerLaw, we proposed a learning method to solve data budgeting problems. To support and systematically evaluate the learning-based method for data budgeting, we curate a large collection of 383 tabular ML datasets, along with their data vs performance curves. Our empirical evaluation shows that it is possible to perform data budgeting given a small pilot study dataset with as few as $50$ data points.

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