When I understand your question correctly you are asking which class is the positive one and which is the negative one.
The answer is that this is to a certain extent arbitrary, so you have to decide that considering the problem at hand.
From "Machine Learning with R" by Brett Lantz, 2.nd edition, 2015, p. 318:
The most common performance measures consider the model's ability to
discern one class versus all others. The class of interest is known as
the positive class, while all others are known as negative.
The use of the terms positive and negative is not intended to imply
any value judgment (that is, good versus bad), nor does it necessarily
suggest that the outcome is present or absent (such as birth defect
versus none). The choice of the positive outcome can even be
arbitrary, as in cases where a model is predicting categories such as
sunny versus rainy or dog versus cat.
The relationship between the positive class and negative class
predictions can be depicted as a 2 x 2 confusion matrix that tabulates
whether predictions fall into one of the four categories:
• True Positive (TP): Correctly classified as the class of
• True Negative (TN): Correctly classified as not the
class of interest
• False Positive (FP): Incorrectly
classified as the class of interest
• False Negative (FN):
Incorrectly classified as not the class of interest
This is the reason that you e.g. have to specify the positive class when using generic performance measure functions, like
ConfusionMatrix in the
caret package in R.
Now another complicating factor is of course your multiclass setting, but this is answered here:
How to compute precision/recall for multiclass-multilabel classification?
In general the most popular approach is to calculate these measures for each class by comparing each class level to the remaining levels (i.e. a "one versus all" approach).