Standarizing the Label to postive intergers in Machine Learning Classification task, why is it recommended?
Please can anybody be kind enough to explain this to me? When doing a
classification task suppose we have 3 targets with notation -2, -1, 0.
I read somewhere it is a good practice to Standardize the labels to
positive integers. In this case suppose we change it to 2, 1, 0. Why
is it recommended?

Can you please link a reference to this!. Usually in practice you wouldn't use integer labels at all. Instead you would use one-hot encoded labels. Here since you have 3 output classes, the neural network will have 3 output nodes. To be concrete the classes can be represented as follows :

0  == &gt;  [1 0 0]
-1  == &gt;  [0 1 0]
-2  == &gt;  [0 0 1]

where one out of the three bits is turned ON. This is because in practice the output of the neural network may be a sigmoid (output: range[0,1]) or softmax function (output: probabilities)