I'm working with a problem where I have a lot of variables for different cases of different users. Depending on the values of the different variables of a concrete user in a concrete case, the algorithm must classify that user in that case as:
But if the user is classified as positive, it must be classified as:
- Positive normal
- Positive high
- Positive extra-high
If a case is positive, depending on the values of a part of the parameters, we know that the probability to be, for example, positive normal is bigger or lower.
To sum up, I see the problem as a spam detector with different spam types.
May this work if I apply an algorithm like:
- Random Forest
- Decision Tree
Or maybe I can include the negative case as a new group and then implement a K-means algorithm? Maybe this would help to find new groups of parameters that will say that the concrete case forms part of a group for sure.
Which one will fit best with a lot of parameters?