Artificial Intelligence
What Logistic Regression Logistic regression is also one type of regression problem. But it is generally used for classification purposes with the help of logistic function. It uses probabilities rather than the actual value. Unlike Linear regression, the dependent variables can only take a limited number of values i.e., the dependent variable is “categorical” rather than the continuous values. When the possible outcome is only two such as spam or not spam. Then it is known as “Binary Logistic Regression”. A Binary logistic model has a dependent variable with two possible values like Pass/Fail, Yes/No, Male/Female, Healthy/Sick, Win/Lose, etc . which are represented as 0 or 1. There may be a “ n ” number of independent variables, each of type Binary or Continuous . Note: The main difference between Linear Regression and Logistic Regression is that the target/ output value which is to be predicted is a Binary value (0 or 1) rather than a numeric value. Some of the examples are: 1