Edit to explain how this is different from the suggested duplicate: Reduce Classification Probability Threshold
My question relates to the same topic, but is thoroughly different, so I'm surprised this was flagged as a duplicate. I want to know, specifically, how I go from looking at an ROC curve and identifying the place on the curve that I'm happy with (after considering my specific use case and all the unique ramifications of this choice), to then:
a) Figure out what threshold value $Z$ generated that point on the curve
b) If possible, change my model parameters to now label all $\hat{p}$ >= $Z$ as positive responders. (I.e, is there a "threshold" parameter in, say, scikit-learn with a default value of 0.5, but which I can change to $Z$, or is that final classification step always something that I have to do manually downstream using output probabilities?)
ORIGINAL QUESTION:
I'm well acquainted with the ROC curve and what it represents. I'm wondering what the next practical steps are in selecting a decision threshold for your model once you've plotted an ROC curve. The axes of the curve don't indicate decision thresholds. So you can point to some coordinates on the curve and say "I like this ratio of TPR/FPR," but then how do you find the decision threshold for that point on the plot?