# Logistic Regression Math & Geometrical Intuition with Example Logistic Regression Math & Geometrical Intuition with Example. Logistic Regression is a Classifier which is used to solve the classification problems. As it’s technically dependent on the Linear Regression & Logit function is a method for a classification problem.

Logistic Regression is the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary). Like all regression analyses, logistic regression is a predictive analysis. Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval, or ratio-level independent variables.

Logistic Regression is a Classifier which is used to solve the classification problems. As it’s technically dependent on the Linear Regression & Logit function is a method for a classification problem.

Why Can’t we use Linear Regression for Classification?

If everything is fine we can use linear regression, why logistic regression is needed.

a. Outlier: If the data set has an outlier, linear regression will not perform better.

b. High-end classification: Can be classified as >1 or <0 which is an issue

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