Hi, i think i have some "missunderstanding" due to the classification
metric in scikit-learn
i have a 2 class problem it is 1.0 or 2.0
precision recall f1-score support
1.0 0.86 0.76 0.81 254
2.0 0.49 0.65 0.56 91
avg / total 0.76 0.73 0.74 345
Specificity: [ 1. * 0.35164835* 0. ]
recall,tpr,sensitivity [ 0. * 0.24015748* 1. ]
# this part is manually computed ( precision, sens, spec, ballanced
accuracy )
logistic regression 0.86,* 0.76, 0.65,* 0.7
The part with:
Specificity: [ 1. 0.35164835 0. ]
recall,tpr,sensitivity [ 0. 0.24015748 1. ]
are computed with
fpr, tpr, thresholds = metrics.roc_curve(expected, predi,
pos_label=1)
print "Specificity:", 1-fpr
print "recall,tpr,sensitivity",tpr
Why is th speceficity for 1-fpr are computed wtih [ 1.
0.35164835 0. ]
and not 0.65 ?
Same with recall
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