Equal Error Rate in Python

Question

Could anybody tell me how could I compute Equal Error Rate(EER) from ROC Curve in python? In scikit-learn there is method to compute roc curve and auc but could not find the met

Answer 1

Copying form How to compute Equal Error Rate (EER) on ROC by Changjiang:

from scipy.optimize import brentq
from scipy.interpolate import interp1d
from sklearn.metrics import roc_curve

fpr, tpr, thresholds = roc_curve(y, y_score, pos_label=1)

eer = brentq(lambda x : 1. - x - interp1d(fpr, tpr)(x), 0., 1.)
thresh = interp1d(fpr, thresholds)(eer)

That gave me correct EER value. Also remember that in the documentation it's written that y is True binary labels in range {0, 1} or {-1, 1}. If labels are not binary, pos_label should be explicitly given and y_score is Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of decisions (as returned by “decision_function” on some classifiers).