Hybrid Model In Machine Learning With Robust Regression For Handling Multicollinearity Outlier In Big Data And Its Application To Agriculture

In this research, 29 independent single variables and 435 independent interaction variables were identified. The limitation of this research were to address the problems such as irrelevant variables, multicollinearity and outliers.

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Main Author:	., Mukhtar
Format:	Thesis
Language:	English
Published:	2023
Subjects:	QA1-939 Mathematics
Online Access:	http://eprints.usm.my/60294/1/Pages%20from%20MUKHTAR%20-%20TESIS.pdf http://eprints.usm.my/60294/
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Institution:	Universiti Sains Malaysia
Language:	English

id	my.usm.eprints.60294
record_format	eprints
spelling	my.usm.eprints.60294 http://eprints.usm.my/60294/ Hybrid Model In Machine Learning With Robust Regression For Handling Multicollinearity Outlier In Big Data And Its Application To Agriculture ., Mukhtar QA1-939 Mathematics In this research, 29 independent single variables and 435 independent interaction variables were identified. The limitation of this research were to address the problems such as irrelevant variables, multicollinearity and outliers. 2023-01 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/60294/1/Pages%20from%20MUKHTAR%20-%20TESIS.pdf ., Mukhtar (2023) Hybrid Model In Machine Learning With Robust Regression For Handling Multicollinearity Outlier In Big Data And Its Application To Agriculture. PhD thesis, Universiti Sains Malaysia.
institution	Universiti Sains Malaysia
building	Hamzah Sendut Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Sains Malaysia
content_source	USM Institutional Repository
url_provider	http://eprints.usm.my/
language	English
topic	QA1-939 Mathematics
spellingShingle	QA1-939 Mathematics ., Mukhtar Hybrid Model In Machine Learning With Robust Regression For Handling Multicollinearity Outlier In Big Data And Its Application To Agriculture
description	In this research, 29 independent single variables and 435 independent interaction variables were identified. The limitation of this research were to address the problems such as irrelevant variables, multicollinearity and outliers.
format	Thesis
author	., Mukhtar
author_facet	., Mukhtar
author_sort	., Mukhtar
title	Hybrid Model In Machine Learning With Robust Regression For Handling Multicollinearity Outlier In Big Data And Its Application To Agriculture
title_short	Hybrid Model In Machine Learning With Robust Regression For Handling Multicollinearity Outlier In Big Data And Its Application To Agriculture
title_full	Hybrid Model In Machine Learning With Robust Regression For Handling Multicollinearity Outlier In Big Data And Its Application To Agriculture
title_fullStr	Hybrid Model In Machine Learning With Robust Regression For Handling Multicollinearity Outlier In Big Data And Its Application To Agriculture
title_full_unstemmed	Hybrid Model In Machine Learning With Robust Regression For Handling Multicollinearity Outlier In Big Data And Its Application To Agriculture
title_sort	hybrid model in machine learning with robust regression for handling multicollinearity outlier in big data and its application to agriculture
publishDate	2023
url	http://eprints.usm.my/60294/1/Pages%20from%20MUKHTAR%20-%20TESIS.pdf http://eprints.usm.my/60294/
_version_	1795011135604260864

Hybrid Model In Machine Learning With Robust Regression For Handling Multicollinearity Outlier In Big Data And Its Application To Agriculture

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