Difference Between Linear and Logistic Regression

In this post, we will understand the difference between linear regression and logistic regression.

Linear Regression

• It helps predict the variable that is continuous, and is a dependent variable.

• This is done using a given set of independent variables.

• It extrapolates a line to find the value of dependent variable.

• Least square methods are used to estimate the accuracy.

• The best fit line is found, that helps predict the output.

• It is generally a continuous value.

• The relation between the dependent variable and independent variable has to be linear.

• The independent variables may have collinearity between them.

• It is considered a machine learning problem, i.e an applied statistics problem.

Logistic Regression

• It helps predict categorical variables.

• It is discrete value.

• It helps solve classification problems.

• It uses the sigmoid function, which is in the form of an ‘S’, to classify the data examples.

• It uses Maximum likelihood estimation to predict values.

• Its output includes values like 0, 1, Yes, No, True, False.

• It doesn’t require the dependent and independent variable to have a linear relationship.

• There shouldn’t be any collinearity between the independent variables.

• It is considered a machine learning problem, i.e an applied statistics problem.