I'm trying to understand ROC curves, however I sometimes read that you can simply go one up for a True positive (TP) and one to the right for a False positive (FP), as mentioned here:
Figure 5.2 shows an example ROC curve—the jagged line—for the sample of test data in Table 5.6. You can follow it along with the table. From the origin,go up two (two positives), along one (one negative), up five (five positives), along one (one negative), up one, along one, up two, and so on. from: Witten et al., data mining: Practical Machine Learning Tools and Techniques
This is easy comprehensible, however there are also ROC curves plotting the FPR vs TPR. I initially thought this would be the same but as far as I can reason the above example does not take False Negatives (FN) and True Negatives (TN) into account, whereas for example the ROC curve below does, as it uses the FPR and TPR (which have the negatives in their divisor)
Then I found the following on WikiPedia:
The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. The true-positive rate is also known as sensitivity, recall or probability of detection1 in machine learning.
Leaving me wondering what the differences are between the above mentioned methods, if there are any at all.
Is a ROC curve made using the 'one up for a TP and one to the right for a FP' method the same as plotting the FPR vs the TPR?