Unlike linear regression which is used to predict the real values of a real entity, logistic regression is used to predict the class or tag of an unseen entry. Logistic regression's output is either a 0 or a 1 depicting the predicted class of the unseen entry. Logistic regression uses a smooth curve whose values range from 0 to 1 for all the values of the independent variable.
Sigmoid function (also called logistic function) is one option for this function. This is defined by the following formula:
The following chart is generated by the code snippet using FsPlot:
You need to install Chrome to get the chart rendered.
So you see that the function value approaches 1 as the value of X approaches infinity, and it approaches 0 as the value of 
approaches negative infinity. So for any given value of , you can determine the class if you set your threshold at 0.5. In other words, you can say that if for a given value of the value of the sigmoid...
