I am referring to the below link and sample, and post the plot diagram from this page where I am confused. My confusion is, there are only 4 threshold, but it seems the roc curve has many data points (> 4 data points), wondering how roc_curve working underlying to find more data points?
http://scikit-learn.org/stable/modules/model_evaluation.html#roc-metrics
>>> import numpy as np
>>> from sklearn.metrics import roc_curve
>>> y = np.array([1, 1, 2, 2])
>>> scores = np.array([0.1, 0.4, 0.35, 0.8])
>>> fpr, tpr, thresholds = roc_curve(y, scores, pos_label=2)
>>> fpr
array([ 0. , 0.5, 0.5, 1. ])
>>> tpr
array([ 0.5, 0.5, 1. , 1. ])
>>> thresholds
array([ 0.8 , 0.4 , 0.35, 0.1 ])
That plot is actually from this example: http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html
As HaohanWang mentioned, the parameter 'drop_intermediate' in function roc_curve can drop some suboptimal thresholds for creating lighter ROC curves. (roc_curve). If set the parameter to be False, all threshold will be displayed, for example: enter image description here all thresholds and corresponding TPRs and FPRs are calculated, but some of them are useless for plotting the ROC curve.
来源：https://stackoverflow.com/questions/39155900/thresholds-in-roc-curve-in-scikit-learn