> For the complete documentation index, see [llms.txt](https://nag-9-s.gitbook.io/machine-learning/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://nag-9-s.gitbook.io/machine-learning/learning/supervised-learning/classification/logistic-regression.md).

# Logistic Regression

<https://www.safaribooksonline.com/library/view/statistics-for-machine/9781788295758/94fde0ee-fc9e-4cbc-aac4-d8dc1d9d94f4.xhtml>

This is the problem in which **outcomes are discrete classes rather than continuous values.** For example, **a customer will arrive or not, he will purchase the product or not, and so on.** In statistical methodology, it uses the maximum likelihood method to calculate the parameter of individual variables. In contrast, in machine learning methodology, log loss will be minimized with respect to *β* coefficients (also known as weights). **Logistic regression has a high bias and a low variance error.**

<https://www.safaribooksonline.com/library/view/statistics-for-machine/9781788295758/a56cd46a-b5db-44a8-962e-c0528f07bb9b.xhtml>

Logistic regression applies maximum likelihood estimation after transforming the dependent variable into a *logit*

variable (natural log of the odds of the dependent variable occurring or not) with respect to independent variables. In this way, logistic regression estimates the probability of a certain event occurring.

what will happen if someone fit the linear regression on a 0-1 problem rather than on logistic regression?

![](https://422781072-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LsbZSeL57mlsVm1bwrG%2F-LsbZa1KWgHxmC9yq5FP%2F-LsbZeCj-CWnil2MbD0G%2Flogistic.png?generation=1572621953693337\&alt=media)