roc - scikit-learn roc_curve: why does it return a threshold value = 2 some time? -


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Correct me if I am wrong: "Threshold" to be a array of scikit learning that the numbers returned by roc_curve are given In [0,1] should, however, sometimes it gives me an array with the first number near "2". Is this a bug or did I wrong? . Thanks 

  In [1]: Import as numpy in NP [2]: from sklearn.metrics to roc_curve import [3]: np.random.seed (11) [4]: Aa = np.random.choice ([true, wrong], 100) [5]: bb = np.random.uniform (0,1,100) [6] in: fpr, tpr, threshold = roc_curve (aa, b B) in [7]: Threshold Out [7]: Array ([1.97396826, 0.97396826, 0.93274463, 0.95941372, 0.93214862, 0.93076592, 0.92960511, 0.92245024, 0.91179548, 0.91112166, 0.87529458, 0.84493853, 0.84068543, 0.83303741, 0.82565223, 0.81096657, 0.80656679, 0.79387241, 0.77054807, 0.76763223, 0.7644911, 0.75964947, 0.73995152, 0.73825262, 0.73466772, 0.73421299, 0.73282534, 0.72391126, 0.71296292, 0.70930102, 0.70116428, 0.69606617, 0.65869235, 0.65670881, 0.65261474, 0.6487222, 0.64805644, 0.64221486, 0.62699782, 0.62522484, 0.62283401, 0.61 601839, 0.611632, 0.59548669, 0.57555854, 0.56828967, 0.55652111, 0.55023947, 0.53825029, 0.53369398, 0.52157349, 0.51900774, 0.50547317, 0.49749635, 0.493913, 0.46154029, 0.45275916, 0.44777116, 0.43822067, 0.43795921, 0.43624093, 0.42039077, 0.41866343, 0.41550367, 0.40032843, 0.36761763, 0.36642721, 0.36567017, 0.36148354, 0.35843793, 0.34371331, 0.33436415, 0.33408289, 0.33387442, 0.31887024, 0.31818719, 0.31367915, 0.30216469, 0.30097917, 0.29995201, 0.28604467, 0.26930354, 0.2383461, 0.22803687, 0.21800338, 0.19301808, 0.16902881, 0.1688173, 0.14491946, 0.13648451, 0.12704826, 0.09141459, 0.08569481, 0.07500199, 0.06288762, 0.02073298, 0.01 934336])    
 
 
 time These boundaries could false to plot the area under the curve against the true positive rate, or plotting Most of Atmk are not used for example in the rate.  
 Still looking like a proper curve, for conspiracy, there is a 0 data point that a threshold is needed because the Scikit-Learn's ROC curve function has normalized probabilities for threshold (someone Even fine score is not required), setting the threshold of this point to 1 is not enough; To set it,  inf  is sensible, but the riders often expect finite data (and it is possible that the implementation also works for integer thresholds). Instead, the implementation uses  maximum (score) + epicilon  where  epsilon = 1  occurs. It may be cosmetic, but you have not told it any problem for some reason!

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