Custom Classification Loss: which is the role of S?

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I have a classification problems with labels 1,..,5.
Since the label is a score related to a grade, I would to compute the Loss by computing the
distance between the triue label and the predicted label
So, if:
are the N points in my dataset,
is the TRUE label of , and if the predicted label is ,
is the weigh for point
is the cost of assigning the point in class to class
I would like to measure the loss ass:
However, how can I use the score to compute the Loss?
The score S contains ngative values. Whats the meaning of that score?
from the explanation in matlab help it seems that the more the S value is low (negative), the more the point is "distant from that class"
If I have 5 labels and for x(i) I have than this means that x(i) would have predicted label = 3
I used kfoldPredict to understand what's happening and it should be right.
Could anyone confirm me?

Accepted Answer

Dinesh Yadav
Dinesh Yadav on 29 Aug 2019
A classification score represents the confidence of the classifier into a class. The range of score depends on ensemble type. For example-
  • · AdaBoostM1 scores range from -inf to inf.
  • · Bag scores range from 0 to 1.
Therefore, if your scores range from -inf to +inf the class corresponding to the highest score is chosen. In the example you gave its label 3. The value 0 is highest among all.
I am providing the link to documentation below for score in kfoldPredict.

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