# Recommending Training Set Sizes for Classification

arXiv: https://arxiv.org/abs/2102.09382 · HTML: https://mlsanity.com/paper/2102.09382/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 35 references that are in the map.
- First posted on arXiv 16 Feb 2021.

## Builds on (most cited first, 2 of 2)

- [How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?](https://mlsanity.com/paper/1511.06348/) (2015)
- [Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose](https://mlsanity.com/paper/1306.5204/) (2013)

## Built on by (most cited first, 2 of 2)

- [Learning Curves for Decision Making in Supervised Machine Learning: A Survey](https://mlsanity.com/paper/2201.12150/) (2022)
- [Data-driven Lake Water Quality Forecasting for Time Series with Missing Data using Machine Learning](https://mlsanity.com/paper/2601.15503/) (2026)

## Abstract (arXiv)

Based on a comprehensive study of 20 established data sets, we recommend training set sizes for any classification data set. We obtain our recommendations by systematically withholding training data and developing models through five different classification methods for each resulting training set. Based on these results, we construct accuracy confidence intervals for each training set size and fit the lower bounds to inverse power low learning curves. We also estimate a sufficient training set size (STSS) for each data set based on established convergence criteria. We compare STSS to the data sets' characteristics; based on identified trends, we recommend training set sizes between 3000 and 30000 data points, according to a data set's number of classes and number of features. Because obtaining and preparing training data has non-negligible costs that are proportional to data set size, these results afford the potential opportunity for substantial savings for predictive modeling efforts.