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Overview of Machine Learning Process Modelling

Much research has been conducted in the area of machine learning algorithms; however, the question of a general description of an artificial learner’s (empirical) performance has mainly remained unanswered. A general, restrictions-free theory on its performance has not been developed yet. In this st...

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Autores principales: Brumen, Boštjan, Černezel, Aleš, Bošnjak, Leon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8469427/
https://www.ncbi.nlm.nih.gov/pubmed/34573748
http://dx.doi.org/10.3390/e23091123
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author Brumen, Boštjan
Černezel, Aleš
Bošnjak, Leon
author_facet Brumen, Boštjan
Černezel, Aleš
Bošnjak, Leon
author_sort Brumen, Boštjan
collection PubMed
description Much research has been conducted in the area of machine learning algorithms; however, the question of a general description of an artificial learner’s (empirical) performance has mainly remained unanswered. A general, restrictions-free theory on its performance has not been developed yet. In this study, we investigate which function most appropriately describes learning curves produced by several machine learning algorithms, and how well these curves can predict the future performance of an algorithm. Decision trees, neural networks, Naïve Bayes, and Support Vector Machines were applied to 130 datasets from publicly available repositories. Three different functions (power, logarithmic, and exponential) were fit to the measured outputs. Using rigorous statistical methods and two measures for the goodness-of-fit, the power law model proved to be the most appropriate model for describing the learning curve produced by the algorithms in terms of goodness-of-fit and prediction capabilities. The presented study, first of its kind in scale and rigour, provides results (and methods) that can be used to assess the performance of novel or existing artificial learners and forecast their ‘capacity to learn’ based on the amount of available or desired data.
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spelling pubmed-84694272021-09-27 Overview of Machine Learning Process Modelling Brumen, Boštjan Černezel, Aleš Bošnjak, Leon Entropy (Basel) Article Much research has been conducted in the area of machine learning algorithms; however, the question of a general description of an artificial learner’s (empirical) performance has mainly remained unanswered. A general, restrictions-free theory on its performance has not been developed yet. In this study, we investigate which function most appropriately describes learning curves produced by several machine learning algorithms, and how well these curves can predict the future performance of an algorithm. Decision trees, neural networks, Naïve Bayes, and Support Vector Machines were applied to 130 datasets from publicly available repositories. Three different functions (power, logarithmic, and exponential) were fit to the measured outputs. Using rigorous statistical methods and two measures for the goodness-of-fit, the power law model proved to be the most appropriate model for describing the learning curve produced by the algorithms in terms of goodness-of-fit and prediction capabilities. The presented study, first of its kind in scale and rigour, provides results (and methods) that can be used to assess the performance of novel or existing artificial learners and forecast their ‘capacity to learn’ based on the amount of available or desired data. MDPI 2021-08-28 /pmc/articles/PMC8469427/ /pubmed/34573748 http://dx.doi.org/10.3390/e23091123 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Brumen, Boštjan
Černezel, Aleš
Bošnjak, Leon
Overview of Machine Learning Process Modelling
title Overview of Machine Learning Process Modelling
title_full Overview of Machine Learning Process Modelling
title_fullStr Overview of Machine Learning Process Modelling
title_full_unstemmed Overview of Machine Learning Process Modelling
title_short Overview of Machine Learning Process Modelling
title_sort overview of machine learning process modelling
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8469427/
https://www.ncbi.nlm.nih.gov/pubmed/34573748
http://dx.doi.org/10.3390/e23091123
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