Exact Logistic Regression Vs Logistic Regression. Linear Regression is used for solving Regression problem. Logistic regression is used for solving Classification problems.
Linear regression typically uses the sum of squared errors while logistic regression uses maximum loglikelihood. The Linear regression models data using continuous numeric value. As against logistic regression models the data in the binary values.
Exact Logistic Regression is used while modeling binary outcome in which log-odds of the outcome are modeled as a linear combination of independent variables.
Key Differences Between Linear and Logistic Regression. Dec 01 2020 The purpose of Linear Regression is to find the best-fitted line while Logistic regression is one step ahead and fitting the line values to the sigmoid curve. Exact Logistic regression can be used while sample size is small for the regular logistic regression. Hence the equation for logistic regression can be developed which is written below.
