Summary
Linear regression, borrowed from statistics, is a crucial algorithm in machine learning that helps understand the relationship between variables and make accurate predictions by minimizing error. With a basic equation Y = MX + B, where M is the slope and B is the y-intercept, linear regression finds the best-fit line by fitting data points to represent the relationship. By extending to multiple dimensions, linear regression can predict outcomes using all variables similarly to two dimensions, with applications like studying the effect of a country's GDP on citizen satisfaction as shown in a graph from Vilnius University.
Introduction to Linear Regression
Linear regression is a fundamental algorithm in machine learning borrowed from statistics to understand the relationship between variables and make accurate predictions by minimizing error.
Basic Form of Linear Regression
In its most basic form, linear regression involves two variables: X and Y, represented as Y = MX + B where M is the slope and B is the y-intercept. The slope determines the translation of the line.
Calculating the Best Fit Line
To find the best fit line in linear regression, calculations are done to fit data points on the line representing the relationship between variables.
Making Predictions Using Linear Regression
Predictions in linear regression are straightforward by solving the equation with new input values to forecast future observations accurately.
Multi-Dimensional Linear Regression
Linear regression can be extended to multiple dimensions by incorporating all variables to predict outcomes in a similar way as in two dimensions.
Application of Linear Regression
Linear regression is commonly used to understand relationships, such as the effect of a country's GDP on citizen satisfaction, as depicted in a study graph from Vilnius University.
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