# What's the relationship between output of qubit measurements and classification of data in Quantum Machine Learning?

I'm training a model in Q# which has more than 2 features.
I have trouble understanding the following things:

• How is the data classified based on the qubit states?

For example: If I have only 2 features (and want to classify our input as class A and class B) then only a single qubit would be used.
After measuring if the qubit turns out to be in the 0 state then its class A and in case of 1 its class B (or vice-versa).

But now if I have say 4 features then 2 qubits would be used and i could have 4 possible outcomes:

$$|00\rangle,\quad |01\rangle,\quad |10\rangle,\quad |11\rangle.$$

So how would the data be classified based on these states?

• Also am I correct in thinking that all the qubits are measured or is only 1 qubit measured and classified as class A/B?