In machine learning, a classification problem is one where you want to predict something, where the something takes on a class value (such as “died” or “survived”) as opposed to a strictly numeric value (such as blood pressure). The variables used to make the prediction are called the features, or the independent variables. For example, to predict whether or not a hospital patient will die or survive, you might use features age, sex, and kidney-test score.
There are several ML classification techniques, for example, logistic regression classification, neural network classification, decision tree classification, and naive Bayes classification. Different classification techniques tend to be suited to different types of problems.
I wrote an article titled “Probit Classification using C#” in the October 2015 issue of MSDN Magazine. See http://msdn.microsoft.com/en-us/magazine/dn802608.aspx. Probit classification is very similar to logistic regression classification. Probit stands for “probability unit” because the result of probit classification is a number between 0 and 1 which can be interpreted as a probability.
Probit classification isn’t used as often as other classification techniques, except by analysts who work in finance and economics. I believe this is mostly for historical reasons. Probit classification tends to give results that are pretty much the same as logistic regression classification.