What is the advantage of logistic regression model over linear regression model?

Logistic regression is easier to implement, interpret, and very efficient to train. If the number of observations is lesser than the number of features, Logistic Regression should not be used, otherwise, it may lead to overfitting. It makes no assumptions about distributions of classes in feature space.

What is difference between Regression and logistic regression?

Linear Regression is a machine learning algorithm based on supervised regression algorithm. Regression models a target prediction value based on independent variables….ML | Linear Regression vs Logistic Regression.

Linear Regression	Logistic Regression
It is based on the least square estimation.	It is based on maximum likelihood estimation.

Where do you use Logistic regression?

Logistic Regression is used when the dependent variable(target) is categorical. For example, To predict whether an email is spam (1) or (0) Whether the tumor is malignant (1) or not (0)

Why Logistic regression is called regression?

Logistic Regression is one of the basic and popular algorithms to solve a classification problem. It is named ‘Logistic Regression’ because its underlying technique is quite the same as Linear Regression. The term “Logistic” is taken from the Logit function that is used in this method of classification.

What is the advantages of linear?

The greatest advantage of the linear model of communication is that the message is clear and unambiguous, leaving the audience with little or no ability to change the message content, style, or presentation.

What is Logistic regression difference between linear and Logistic regression?

Linear Regression is used to handle regression problems whereas Logistic regression is used to handle the classification problems. Linear regression provides a continuous output but Logistic regression provides discreet output.