Abstract
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.
Keywords
Proper linear modelLinear regressionPolynomial regressionRegression diagnosticRegression analysisSimple linear regressionLinear predictor functionRegressionBayesian multivariate linear regressionComputer scienceLinear modelMachine learningLocal regressionMultivariate adaptive regression splinesStatisticsArtificial intelligenceCross-sectional regressionMathematics
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Publication Info
- Year
- 2020
- Type
- review
- Volume
- 1
- Issue
- 2
- Pages
- 140-147
- Citations
- 1061
- Access
- Closed
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Cite This
Dastan Hussen Maulud,
Adnan Mohsin Abdulazeez
(2020).
A Review on Linear Regression Comprehensive in Machine Learning.
Journal of Applied Science and Technology Trends
, 1
(2)
, 140-147.
https://doi.org/10.38094/jastt1457
Identifiers
- DOI
- 10.38094/jastt1457