A Survey of The State-Of-The-Art AutoML Tools and Their Comparisons
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Keywords:
Machine Learning, Automated Machine Learning, AutoML, Hyperparameter Optimization, Model SelectionAbstract
Machine learning is used effectively in many areas today and its usage area is increasing day
by day. In addition, processes based on machine learning are also developing in a technology-oriented
manner, and users are gaining new perspectives on solving current problems. While machine learning
makes predictions about stocks in the financial sector, it also plays an active role in early diagnosis of
diseases in the healthcare sector. It is actively used in route calculation and defective product detection in
the field of production and logistics, and in situations such as analysis of customer behavior and product
recommendations in the shopping sector. AutoML can be defined as a process that aims to automate the
machine learning process end-to-end. It enables the machine learning process to be accelerated by
automating especially time-consuming tasks that work with the logic of repetition, and it allows people
who work in this field to create more efficient and productive models. In addition, AutoML helps users
who are not experts in this field in the stages of machine learning model development, data management,
analysis and evaluation of their own data, by providing various conveniences to users in model training
and subsequent stages. In this article, after discussing what AutoML is, AutoML processes and areas of
use, information about various AutoML platforms have been given, the differences between widely used
AutoML platforms will be evaluated, and their advantages and disadvantages compared to each other will
be included.
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