What is the difference between logistic and linear regression?

Question

Accepted Answer

Logistic regression is used for binary classification, while linear regression is used for predicting continuous values.

Logistic regression is a classification algorithm, while linear regression is a regression algorithm.
Logistic regression uses a logistic function to model the probability of the binary outcome.
Linear regression uses a linear function to model the relationship between the independent and dependent variables.
Logistic regression predicts discrete outcomes (e.g., yes/no, true/false), while linear regression predicts continuous values (e.g., predicting house prices).

Accepted Answer

Linear regression is used to predict the continuous dependent variable using a given set of independent variables. Logistic Regression is used to predict the categorical dependent variable using a given set of independent variables. Linear Regression is used for solving Regression problem.

Accepted Answer

Linear regression is used for predicting a continuous variable and logistic regression is used for predicting a categorical variable. The fitted function in the linear regression equation of a straight line and the fitted function in logistic regression is the sigmoid function. The cost function in linear regression is OLS (ordinary least square) and the cost function in logistic regression is the log loss function.

Accepted Answer

Linear regression consists of a linear mathematical equation between variables and weights, which is primarily used to predict a linear relationship between the dependent(target) and the independent variables.

On the other hand, Logistic is quite similar to linear on the basis of the linear mathematical equation but that equation is converted into mathematical probabilities such that the range of the out of the linear equation if transformed to zero to 1 using a logit function.

What is the difference between logistic and linear regression?

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