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...