10 Most Common Machine Learning Algorithms Explained -2023
Linear regression is a statistical method used to examine the relationship between two continuous variables: one independent variable and one dependent variable. The goal of linear regression is to find the best-fitting line through a set of data points, which can then be used to make predictions about future observations.
where y is the dependent variable, x is the independent variable, b0 is the y-intercept (the point at which the line crosses the y-axis), and b1 is the slope of the line. The slope represents the change in y for a given change in x.
To determine the best-fitting line, we use the method of least squares, which finds the line that minimizes the sum of the squared differences between the predicted y values and the actual y values.
Linear regression can also be extended to multiple independent variables, known as multiple linear regression. The equation for a multiple linear regression model is:
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