# Data classification

If clustering relates to learning without a teacher, then **classification**, on the contrary, is knowing to what groups a part of the known data belongs, and we want to determine the probability with which the unknown new element might belong to one group or another.

For example, using the `Classify`

function, let's try to explain which are even numbers and which are odd numbers:

We have set several even and several odd numbers, then we have classified them with default parameters using the `Classify`

function, and finally we can see how the missing elements, 5, 0 and 10, will be classified. As you can see from the result, they were successfully and correctly defined:

With the help of the `Probabilities`

parameter, you can determine how likely it is for an element to belong to a particular class.

The `ClassifierInformation`

function provides information about the sample data based on which the classification took place:

In Mathematica, there are also built-in classes from different...