R Data Mining Blueprints by 2016

Author:2016
Language: eng
Format: epub
Publisher: Packt Publishing

Logistic regression

The linear regression model based on the ordinary least square method assumes that the relationship between the dependent variable and the independent variables is linear; however, the logistic regression model assumes the relationship to be logarithmic. There are many real-life scenarios where the variable of interest is categorical in nature, such as buying a product or not, approving a credit card or not, tumor is cancerous or not, and so on. Logistic regression not only predicts a dependent variable class but it predicts the probability of a case belonging to a level in the dependent variable. The independent variables need not be normally distributed and need not have equal variance. Logistic regression belongs to the family of generalized linear regression. If the dependent variable has two levels then logistic regression can be applied, but if it has more than two levels, such as high, medium, and low, then multinomial logistic regression model can be applied. All the independent variables can be continuous, categorical, or nominal.

The logistic regression model can be explained using the following equation:

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