I understand that the ROC curve will plot the sensitivity vs FPR for varying thresholds.
For my SVM ML model, I desire a good sensitivity score so I have decreased the threshold to make a positive classification to 0.40. I know this threshold will increase the sensitivity but decrease the PPV and specificity.
The AUCROC for the default threshold SVM model is much higher (AUROC = 0.80) than the AUROC for the SVM model with the adjusted threshold of 0.40 (AUROC = 0.73).
This does not make sense to me because the ROC curve is a plot of the sensitivity vs FPR for varying thresholds. Shouldn't the ROC Curve and AUROC score be identical between the default svm model and the svm model with threshold = 0.40.
Or is it that I don't correctly understand what the ROC curve is plotting?
Here is the code I used:
prob_preds = clf.predict_proba(x_test)
threshold = 0.40
preds = [1 if prob_preds[i] > threshold else 0 for i in range(len(prob_preds))]
fpr, tpr, thresholds = roc_curve(y_test, preds)