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The following are common metrics used to evaluate the performance of a linear regression model: The strength of any linear regression model can be assessed using various evaluation metrics. This can be done using analytical methods, such as the normal equation, or numerical optimization methods, such as gradient descent. To minimize MSE, you need to find the values of the coefficients (slope and intercept) that minimize the sum of squared residuals (differences between the observed and predicted values). The best-fit line is determined by finding the values of the model’s parameters that minimize the MSE. The most commonly used cost function for linear regression is the mean squared error (MSE) which calculates the average of the squares of the differences between the predicted and actual values. The goal is to minimize the cost function to obtain the best-fit line that accurately represents the relationship between the independent and dependent variables. In linear regression, the cost function measures the difference between the predicted values (obtained from the regression line) and the actual target values. Cost FunctionĪ cost function is a mathematical representation of the difference between the predicted values and the actual values in a model. However, it is still widely used as a simple and effective tool for predictive modelling in a variety of applications. Linear Regression assumes that the relationship between the dependent variable and the independent variables is linear and has some limitations in modelling complex relationships.