A Review on Linear Regression Comprehensive in Machine Learning
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2020-12-31
SCID: 54.1/zdcx694t
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explanatory variableslinear regressionmultiple linear regressionpolynomial regressionpredictive modeling
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Abstract (AI)
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.
Key Findings
1
Linear regression is presented as a widely used statistical and machine-learning method for modeling linear relationships between predictors and outcomes.
2
Most reviewed studies focus on datasets and evaluate model efficiency by correlating predictions with actual explanatory-variable values.
3
The paper surveys studies applying linear and polynomial regression, comparing approaches based on prediction and precision optimization.
4
The review distinguishes two main forms of linear regression: simple linear regression and multiple linear regression.
Research Object
linear and polynomial regression models in machine learning
Research Subject
their prediction accuracy and performance optimization across datasets
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2020-12-31
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