Logistic regression


In logistic regression, we trying to get a final outcome which is either,

a) TRUE / FALSE

b) Between 1 to 0

Perhaps the following equation would paint the picture a little better.


y = 1 / e^- (a + b1x1 + b2x2 + x3b3...)

As you can see from the equation above, we will be getting a result  0 to 1 or true / false, depending on how you calculate it.

We will be using our data earlier and call a very important function in R, glm with family parameter set to binomial as shown below :-





And the result is as follows. Here can can see that degree is a strong influence of whether a person gets high salary or not.




Residue deviance - is lack of fit of your model taken as a whole.

Fisher scoring iteration is to say how a model is estimated.


As for the interpretation of results, here is one great link.




Comments

Popular posts from this blog

OpenCover code coverage for .Net Core

Android Programmatically apply style to your view

Using Custom DLL with IronPython / Scripts