Machine Learning Models for Statistical Analysis

Marko Grebovic; Luka Filipovic; Ivana Katnic; Milica Vukotic; Tomo Popovic

doi:10.34028/iajit

Article Details

Machine Learning Models for Statistical Analysis

Authors:

Marko Grebovic | Luka Filipovic | Ivana Katnic | Milica Vukotic | Tomo Popovic

Publisher:

Zarqa University, Jordan

Journal:

The International Arab Journal of Information Technology (IAJIT) Vol.20 Issue 3, pp.505-514

Document Type:

Article

Publication Date:

1/1970

Document Language:

English

Document ID (Org No.):

2001388654

pISSN:

2309-4524

DOI:

10.34028/iajit

Keywords:

Abstract:

 Compared to traditional statistical models, Machine Learning (ML) algorithms provide the ability to interpret, understand and summarize patterns and regularities in observed data for making predictions in an advanced and more sophisticated way. The main reasons for the advantage of ML methods in making predictions are a small number of significant predictors of the statistical models, which means limited informative capability, and pseudo-correct regular statistical patterns, used without previous understanding of the used data causality. Also, some ML methods, like Artificial Neural Networks, use non-linear algorithms, considering links and associations between parameters. On the other hand, statistical models use one- step-ahead linear processes to improve only short-term prediction accuracy by minimizing a cost function. Although designing an optimal ML model can be a very complex process, it can be used as a potential solution for making improved prediction models compared to statistical ones. However, ML models will not automatically improve prediction accuracy, so it is necessary to evaluate and analyze several statistical and ML methods, including some artificial neural networks, through accuracy measures for prediction purposes in various fields of applications. A couple of techniques for improving suggested ML methods and artificial neural networks are proposed to get better accuracy results.

Article Details

Machine Learning Models for Statistical Analysis

Authors:

Marko Grebovic | Luka Filipovic | Ivana Katnic | Milica Vukotic | Tomo Popovic

Publisher:

Zarqa University, Jordan

Journal:

The International Arab Journal of Information Technology (IAJIT) Vol.20 Issue 3, pp.505-514

Document Type:

Article

Publication Date:

1/1970

Document Language:

English

Document ID (Org No.):

2001388654

pISSN:

2309-4524

DOI:

10.34028/iajit

Machine learning

artificial neural networks

statistical models

accuracy measures

Issues

Article Details

Machine Learning Models for Statistical Analysis

Authors: Marko Grebovic | Luka Filipovic | Ivana Katnic | Milica Vukotic | Tomo Popovic

Publisher: Zarqa University, Jordan

Journal: The International Arab Journal of Information Technology (IAJIT) Vol.20 Issue 3, pp.505-514

Document Type: Article

Publication Date: 1/1970

Document Language: English

Document ID (Org No.): 2001388654

pISSN: 2309-4524

DOI: 10.34028/iajit

Machine learning

artificial neural networks

statistical models

accuracy measures

Issues

Authors:

Marko Grebovic | Luka Filipovic | Ivana Katnic | Milica Vukotic | Tomo Popovic

Publisher:

Zarqa University, Jordan

Journal:

The International Arab Journal of Information Technology (IAJIT) Vol.20 Issue 3, pp.505-514

Document Type:

Article

Publication Date:

1/1970

Document Language:

English

Document ID (Org No.):

2001388654

pISSN:

2309-4524

DOI:

10.34028/iajit