# Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools

arXiv: https://arxiv.org/abs/1908.05557 · HTML: https://mlsanity.com/paper/1908.05557/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2019.

## Built on by (most cited first, 25 of 25)

- [AMLB: an AutoML Benchmark](https://mlsanity.com/paper/2207.12560/) (2022)
- [Benchmarking Multimodal AutoML for Tabular Data with Text Fields](https://mlsanity.com/paper/2111.02705/) (2021)
- [Selecting and Composing Learning Rate Policies for Deep Neural Networks](https://mlsanity.com/paper/2210.12936/) (2022)
- [Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey](https://mlsanity.com/paper/2101.10181/) (2021)
- [Automated machine learning: AI-driven decision making in business analytics](https://mlsanity.com/paper/2205.10538/) (2022)
- [An Empirical Study on the Usage of Automated Machine Learning Tools](https://mlsanity.com/paper/2208.13116/) (2022)
- [AutoMLBench: A Comprehensive Experimental Evaluation of Automated Machine Learning Frameworks](https://mlsanity.com/paper/2204.08358/) (2022)
- [Automated Machine Learning Techniques for Data Streams](https://mlsanity.com/paper/2106.07317/) (2021)
- [Automating Generative Deep Learning for Artistic Purposes: Challenges and Opportunities](https://mlsanity.com/paper/2107.01858/) (2021)
- [Classification of integers based on residue classes via modern deep learning algorithms](https://mlsanity.com/paper/2304.01333/) (2023)
- [STREAMLINE: A Simple, Transparent, End-To-End Automated Machine Learning Pipeline Facilitating Data Analysis and Algorithm Comparison](https://mlsanity.com/paper/2206.12002/) (2022)
- [Explainable Automated Machine Learning for Credit Decisions: Enhancing Human Artificial Intelligence Collaboration in Financial Engineering](https://mlsanity.com/paper/2402.03806/) (2024)
- [Problem-oriented AutoML in Clustering](https://mlsanity.com/paper/2409.16218/) (2024)
- [EDCA - An Evolutionary Data-Centric AutoML Framework for Efficient Pipelines](https://mlsanity.com/paper/2503.04350/) (2025)
- [MicroNAS: An Automated Framework for Developing a Fall Detection System](https://mlsanity.com/paper/2504.07397/) (2025)
- [Evaluation of Representation Models for Text Classification with AutoML Tools](https://mlsanity.com/paper/2106.12798/) (2021)
- [Man versus Machine: AutoML and Human Experts' Role in Phishing Detection](https://mlsanity.com/paper/2108.12193/) (2021)
- [A Survey on Proactive Customer Care: Enabling Science and Steps to Realize it](https://mlsanity.com/paper/2110.05015/) (2021)
- [DiscoVars: A New Data Analysis Perspective -- Application in Variable Selection for Clustering](https://mlsanity.com/paper/2304.03983/) (2023)
- [Comparing AutoML and Deep Learning Methods for Condition Monitoring using Realistic Validation Scenarios](https://mlsanity.com/paper/2308.14632/) (2023)
- [A knowledge-driven AutoML architecture](https://mlsanity.com/paper/2311.17124/) (2023)
- [Couler: Unified Machine Learning Workflow Optimization in Cloud](https://mlsanity.com/paper/2403.07608/) (2024)
- [Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance](https://mlsanity.com/paper/2506.23314/) (2025)
- [An AutoML Framework using AutoGluonTS for Forecasting Seasonal Extreme Temperatures](https://mlsanity.com/paper/2509.17734/) (2025)
- [InFusionLayer: a CFA-based ensemble tool to generate new classifiers for learning and modeling](https://mlsanity.com/paper/2603.10049/) (2026)

## Abstract (arXiv)

There has been considerable growth and interest in industrial applications of machine learning (ML) in recent years. ML engineers, as a consequence, are in high demand across the industry, yet improving the efficiency of ML engineers remains a fundamental challenge. Automated machine learning (AutoML) has emerged as a way to save time and effort on repetitive tasks in ML pipelines, such as data pre-processing, feature engineering, model selection, hyperparameter optimization, and prediction result analysis. In this paper, we investigate the current state of AutoML tools aiming to automate these tasks. We conduct various evaluations of the tools on many datasets, in different data segments, to examine their performance, and compare their advantages and disadvantages on different test cases.