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It measures how a linear regression model is performing. Cost function optimizes the regression coefficients or weights.The different values for weights or coefficient of lines (a 0, a 1) gives the different line of regression, and the cost function is used to estimate the values of the coefficient for the best fit line.The different values for weights or the coefficient of lines (a 0, a 1) gives a different line of regression, so we need to calculate the best values for a 0 and a 1 to find the best fit line, so to calculate this we use cost function. The best fit line will have the least error. When working with linear regression, our main goal is to find the best fit line that means the error between predicted values and actual values should be minimized. If the dependent variable decreases on the Y-axis and independent variable increases on the X-axis, then such a relationship is called a negative linear relationship. If the dependent variable increases on the Y-axis and independent variable increases on X-axis, then such a relationship is termed as a Positive linear relationship. A regression line can show two types of relationship: If more than one independent variable is used to predict the value of a numerical dependent variable, then such a Linear Regression algorithm is called Multiple Linear Regression.Ī linear line showing the relationship between the dependent and independent variables is called a regression line. If a single independent variable is used to predict the value of a numerical dependent variable, then such a Linear Regression algorithm is called Simple Linear Regression. Linear regression can be further divided into two types of the algorithm:
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The values for x and y variables are training datasets for Linear Regression model representation. X= Independent Variable (predictor Variable)Ī0= intercept of the line (Gives an additional degree of freedom)Ī1 = Linear regression coefficient (scale factor to each input value).