I have read this question but it doesn't have any well-explained answer for this case: I understand the ROC curve in overall, but I'm looking for a step-to-step explanation in order to understand how the curve changes when one true-positive becomes false-positive (and so on). For example, how can I draw a ROC curve with this set?. Does the score means the "certainty" for the value given? (for example: for the id == A the is a 0.03 of certainty that the value is active/decoy == a?)

id  score   active/decoy 
A   0.03    a
B   0.08    a
C   0.10    d
C   0.11    a
D   0.22    d
E   0.32    a
F   0.35    a
G   0.43    a
H   0.57    a
I   0.61    d
J   0.68    a
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    $\begingroup$ Did you read this question? stats.stackexchange.com/questions/145566/… Can you be more specific about which part you do not understand? $\endgroup$ – Sycorax Feb 20 at 21:09
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    $\begingroup$ Start with a cutoff of 0.001. If the score is above the cutoff, mark is "d". Otherwise, mark it "a". Determine the sensitivity and 1-specificity. Do it again for a cutoff of 0.002. Do it again for a cutoff... $\endgroup$ – Dave Feb 20 at 21:09

Here is a general algorithm I have used in the past to draw ROC

  1. Sort the data by score from lowest to highest
  2. Choose lowest score as the cut-point
  3. Calculate sensitivity and specificity using the cut-point from step 2. Save sensitivity and 1-specificity
  4. Repeat steps 2 and 3 for each unique score from lowest to highest
  5. Create a step plot where the x-axis is 1-specificity and the y-axis is sensitivity

I would recommend re-coding active/decoy as 0/1 for the algorithm

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