I've a pooled group of individuals and a given number of features. So that my matrix looks like:
| individuals | feature1 | feature2 | feature3 |
|---|---|---|---|
| bob | 1 | 0 | 1 |
| ralph | 0 | 1 | 1 |
| mark | 1 | 0 | 1 |
I want to discriminate between bob, ralph and mark. I'm not interested much in the features, I just want to know this line is ralph and not bob or mark. And I want to be quite accurate about it.
Could I use Naive classifier for this? I'd like to use because I may have some prior knowledge to include. Like 50% is bob, 25% is ralph and 25% is mark.
In this case I won't have a training and test data. Could I train the classifier on this data and use it to deconvolute as well? I won't use it in prediction on other data, but just to deconvolute this dataset. If not, can I get some reading to understand why?
