Machine Learning in Python Essential Techniques for Predictive Analysis by Michael Bowles (z-lib.org).epub

A unique identifier may or may not be present in your problem. For
instance, websites typically tag site visitors with a user ID that is
associated with them for the duration of their visit. If a user does not
register with the site, the same user gets a different ID with each visit.
The ID is usually assigned to each observation, which will be the
subject of the prediction you’re going to build. Columns 2, 3, and 4
are called Attributes instead of being given more specific names like
Height or Gender. The point is to highlight their role in the prediction
process. Attributes are data available about the case that will be used
to make predictions.
Labels are the things you want to predict. In this example, UserID is a
simple number, Attribute 1 is height, Attribute 2 is gender, and
Attribute 3 is how many books the person read last year. The column
under Labels contains how much money the individual spent on
books online last year. What are the roles that these different
categories of data will play? What use does a machine learning
algorithm make of user ID, attributes, and labels? The short answer is
this: You ignore the user ID. You use the attributes to predict the
labels.
The unique ID is for bookkeeping purposes and allows you to refer
back to the other data available for the specific case. Generally, the
unique ID does not get used directly in a machine learning algorithm.
Attributes are the things that you’ve chosen to use for making
predictions. Labels are observed outcomes that the machine learning
algorithm will use to build a predictive model.
User ID doesn’t usually get used for making predictions because it is
too specific. It pertains to only a single example. The trick with
machine learning is to build a model that generalizes to new cases
(not merely memorizing past cases). To achieve that, the algorithm
must be derived so that it is forced to pay attention to more than one
row of data. One possible exception to excluding user ID is when the
user ID is numeric and assigned in the order that users are signed up.
Basically, it’s indicating signup date in that case and can be useful
because users with close IDs signed up at similar times and can be
considered as a group on that basis.