Difference Between Linear and Logistic Regression

Machine LearningArtificial IntelligenceSoftware & Coding

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.

raja
Published on 25-Mar-2021 05:50:40
Advertisements