Data Science

 

Logistic Regression

 

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The logistic model is a statistical model that models the log-odds of an event as a linear combination of one or more independent variables.

In regression analysis, logistic regression estimates the parameters of a logistic model (the coefficients in the linear or non linear combinations).
In binary logistic regression there is a single binary dependent variable, coded by an indicator variable, where the two values are labeled "0" and "1", while the independent variables can each be a binary variable (two classes, coded by an indicator variable) or a continuous variable (any real value).

 

An explanation of logistic regression can begin with an explanation of the standard logistic function. The logistic function is a sigmoid function, which takes any real input t, and outputs a value between zero and one.

This is interpreted as taking input log-odds and having output probability.
The standard logistic function σ:R→(0,1) is defined as follows:


A graph of the logistic function on the t-interval (−6,6) will be like:

 

 

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