Why does sklearn.roc_curve return varying shapes for fpr and tpr and thresholds with varying random states?
the internal algorithm eliminates repeated scores from thresholds, so if you have repeated entries whose scores are exactly equal then they will be removed.
In sklearn.roc_curve the results returned are fpr, tpr and thresholds.
Despite drop_intermediate set to False, the shapes of fpr, tpr and thresholds
change with random states.
Why is that?
As an example, I have:

test_labels and predicted_probabilities are (158,).
fpr, tpr and thresholds are (149,), in another run they are (146,).