Semantic Scholar Open Access 2020 1018 sitasi

A Review on Linear Regression Comprehensive in Machine Learning

Dastan Maulud A. Abdulazeez

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Abstrak

Perhaps one of the most common and comprehensive statistical and machine learning algorithms are linear regression. Linear regression is used to find a linear relationship between one or more predictors. The linear regression has two types: simple regression and multiple regression (MLR). This paper discusses various works by different researchers on linear regression and polynomial regression and compares their performance using the best approach to optimize prediction and precision. Almost all of the articles analyzed in this review is focused on datasets; in order to determine a model's efficiency, it must be correlated with the actual values obtained for the explanatory variables.

Penulis (2)

Dastan Maulud

A. Abdulazeez

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APA MLA BibTeX

Maulud, D., Abdulazeez, A. (2020). A Review on Linear Regression Comprehensive in Machine Learning. https://doi.org/10.38094/jastt1457

Akses Cepat

Lihat di Sumber doi.org/10.38094/jastt1457

Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Total Sitasi: 1018×
Sumber Database: Semantic Scholar
DOI: 10.38094/jastt1457
Akses: Open Access ✓