Data Budgeting for Machine Learning

TL;DR

This paper addresses the challenge of data budgeting in machine learning by proposing a learning-based approach to predict performance saturation and data requirements, supported by a large curated dataset and empirical validation.

Contribution

It introduces a novel learning method for data budgeting, moving beyond traditional PowerLaw approaches, and provides a comprehensive dataset for evaluation.

Findings

Data budgeting is feasible with small pilot datasets.

The proposed method accurately predicts performance saturation.

Empirical results validate the effectiveness of the learning-based approach.

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.