What is the difference between Linear Regression and Logistic Regression?

Question

Accepted Answer

Linear Regression is used for predicting continuous numerical values, while Logistic Regression is used for predicting binary categorical values.

Linear Regression predicts a continuous output, while Logistic Regression predicts a binary output.
Linear Regression uses a linear equation to model the relationship between the independent and dependent variables, while Logistic Regression uses a logistic function.
Linear Regression assumes a linear relationship between the variables, while Logistic Regression assumes a non-linear relationship.
Linear Regression uses the least squares method to minimize the sum of squared errors, while Logistic Regression uses maximum likelihood estimation.
Linear Regression is used for tasks like predicting house prices, while Logistic Regression is used for tasks like predicting whether a customer will churn or not.

Accepted Answer

Linear Regression predicts continuous values (e.g., price), while Logistic Regression predicts categories (e.g., yes/no) using probabilities

Accepted Answer

linear regression is used for regression problems ,used to predict continuos dependent variables .

output is continuos while logistic regression is used for classification problems(sigmoid curve) it gives discrete output .it is used to predict categorical dependent variables .

What is the difference between Linear Regression and Logistic Regression?

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