# Leveraging Automated Machine Learning for Text Classification: Evaluation of AutoML Tools and Comparison with Human Performance

Source: [Leveraging Automated Machine Learning for Text Classification: Evaluation of AutoML Tools and Comparison with Human Performance](<https://www.sota2.com/research/paper/68fa68e11e209131cea1eeb1>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:48:52.480Z

Authors: Matthias Blohm, Marc Hanussek, Maximilien Kintz

Published: 2020-12-07T10:31:13.000Z

## Abstract

Recently, Automated Machine Learning (AutoML) has registered increasing success with respect to tabular data. However, the question arises whether AutoML can also be applied effectively to text classification tasks. This work compares four AutoML tools on 13 different popular datasets, including Kaggle competitions, and opposes human performance. The results show that the AutoML tools perform better than the machine learning community in 4 out of 13 tasks and that two stand out.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Multimodal AutoML on Multimodal Benchmark](<https://www.sota2.com/research/sota/multimodal-automl-on-multimodal-benchmark.md>): Multimodal AutoML; Multimodal Benchmark; Prod Score: 90.7.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2012.03575>)

[PDF](<https://arxiv.org/pdf/2012.03575.pdf>)