The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A Review on Linear Regression Comprehensive in Machine Learning
Open accessAbstract
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.
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Citations
Cites
A few useful things to know about machine learning
Domingos, P. (2012) · Communications of the ACM · doi:10.1145/2347736.2347755 · Cited by 3,326
Understanding Machine Learning: From Theory to Algorithms
Shalev-Shwartz, S., & Ben-David, S. (2014) · doi:10.1017/cbo9781107298019 · Cited by 3,115
Cited by
Classification Based on Decision Tree Algorithm for Machine Learning
Charbuty, B., & Abdulazeez, A. (2021) · Journal of Applied Science and Technology Trends · doi:10.38094/jastt20165 · Cited by 1,871
Self-citationHan, W., Zhang, X., Wang, Y., Wang, L., Huang, X., Li, J., Wang, S., Chen, W., Li, X., Feng, R., et al. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.05.032 · Cited by 303
Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey
Putrada, A. G., Abdurohman, M., Perdana, D., & Nuha, H. H. (2022) · IEEE Access · doi:10.1109/access.2022.3169765 · Cited by 101
“Among the existing regression analysis methods, the simplest and most commonly used way is linear regression [194].”
MethodAbdelfattah, E., Malik, M., & Muhammad Ishraque Osman, S. (2025) · IEEE Access · doi:10.1109/access.2025.3579712 · Cited by 8
Metadata
- Authors
- Maulud, D., & Abdulazeez, A. M.
- Venue
- Journal of Applied Science and Technology Trends
- Published
- 2020-12-31
- Volume
- 1
- Issue
- 2
- Pages
- 140-147
- ISSN
- 2708-0757 (ISSN-L)
- OpenAlex
- W3118299338
- Cited by
- 1,262
- References
- 2
- FWCI
- 67.54
- Type
- journal article
- Language
- en
- Open access
- diamond
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
