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

## TL;DR

This paper evaluates and compares various AutoML tools to assess their effectiveness in automating key machine learning tasks across multiple datasets, highlighting their strengths and limitations.

## Contribution

It provides a comprehensive evaluation of current AutoML tools, offering insights into their performance and practical applicability in automating ML pipeline tasks.

## Key findings

- AutoML tools vary significantly in performance across datasets
- Some tools excel in data preprocessing and feature engineering
- Trade-offs exist between automation level and model accuracy

## Abstract

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.