Linear regression
Introduction
1. Linear regression (LR) attempts to model the relationship between two variables by fitting a linear
equation to the observed data. One variable is considered to be an explanatory variable, and the other
is considered to be a dependent variable.
2. A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable
and Y is the dependent variable. The slope of the line is b, and a is the intercept.
On ViNAS, the LR algorithms is implemented using scikit-learn 0.24.1.
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