# Who Invented Logistic Regression?

## 1- Logistic Regression History

Logistic Regression was invented by Pierre François Verhulst in 1838 and he introduced it in his paper **Correspondance of Mathematics and Physics** which can be accessed using following link from archive.org:

Verhulst was a Belgian man who was inspired and influenced by another world-changing Belgian mathematician Adolphe Quetelet.

In 1845 Verhulst went on to share an updated version of Logistic Regression he introduced in 1838 in the following paper:

- Recherches mathématiques sur la loi d’accroissement de la population

**GLM**.

## 2- Modern Day Applications of Logistic Regression

Logit model which is commonly used today in Logistic Regression implementations was found by McFadden in 1973 who then received a Nobel prize for this discovery in 2000. Logit model makes the assumption that error in the logistic regression is a distribution based on function of logistic density.

Logit transforms values of a line to a logistic curve by using which dependent variables (labels or target values) can only take binary values of 0 and 1.

You can find MacFadden’s original logit paper along with his other work in his personal website from Berkeley University below:

## 3- Summary

In this article we have covered the history of Logistic Regression, a machine learning algorithm still commonly used in 2020s, nearly 180 years after its initial discovery.