I am working with scikit-learn for classification problem to predict Win or Loss of an opportunity.

I used the piece of code:

`fpr, tpr, thresholds =roc_curve(yTest,predictions)`

And the result is:

```
(array([ 0. , 0.2628946, 1. ]),
array([ 0. , 0.73692477, 1. ]),
array([2, 1, 0]))
```

I am aware of calculating the AUC using the fpr, tpr for various thresholds varying in the range (1,0). Ideally, what I know is thresold should be in between 1 and 0.

But, here the threshold values are 2,1,0. What to understand from this and how to interpret this.

The sample code looks fine:

import numpy as np

from sklearn import metrics

y = np.array([1, 1, 2, 2])

scores = np.array([0.1, 0.4, 0.35, 0.8])

fpr, tpr, thresholds = metrics.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 ])

My predict_proba(yTest) are, These are raw probabilities from Random Forest:

[ 0.09573287 0.90426713]

[ 0.14987409 0.85012591]

[ 0.16348188 0.83651812]

…,

[ 0.13957409 0.86042591]

[ 0.04478675 0.95521325]

[ 0.03492729 0.96507271]