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Compute specificity and sensitivity at certain thresholds
The 2019 Stack Overflow Developer Survey Results Are In
Unicorn Meta Zoo #1: Why another podcast?
Announcing the arrival of Valued Associate #679: Cesar Manara
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsRelationship between VC dimension and degrees of freedomCompute Baseline/Representative of Time-Series DataIs it possible using tensorflow to create a neural network that maps a certain input to a certain output?Why doesn't overfitting devastate neural networks for MNIST classification?Early stopping and boundsHow Natural language processing and elasticsearch are relatedROC-AUC curve as metric for binary classifier without machine learning algorithmUsing Random Forest Probabilities for customer sensitivityHow to compute G-mean score?How to balance specificity and sensitivity?
$begingroup$
I have the following table with predictive probabilities and true class labels:
beginarray
hline
P(T=1) &0.54& 0.23 & 0.78 & 0.88 & 0.26 & 0.41 & 0.90 & 0.45&0.19&0.36 \ hline
T&1&0 &0 &1 &0 &0& 1& 1& 0& 0\ hline
endarray
The question is to compute the specificity & sensitivity at the threshold of 0.5.
My attempt at answering this question:
Sensitivity = true positive rate[P(T=1) > 0.5]
= (0.54 + 0.88 + 0.9)/4 = 0.58
Specificity = 1-false positive rate[P(T=1) > 0.5]
= 1- [(0.78)/6]
= 0.87
Not sure if my working above is correct. I would appreciate if someone can guide me to the correct solution. Thanks.
classification self-study
asked Mar 30 at 16:04
vic12vic12
132
$endgroup$
add a comment |
$begingroup$
I have the following table with predictive probabilities and true class labels:
beginarray
hline
P(T=1) &0.54& 0.23 & 0.78 & 0.88 & 0.26 & 0.41 & 0.90 & 0.45&0.19&0.36 \ hline
T&1&0 &0 &1 &0 &0& 1& 1& 0& 0\ hline
endarray
The question is to compute the specificity & sensitivity at the threshold of 0.5.
My attempt at answering this question:
Sensitivity = true positive rate[P(T=1) > 0.5]
= (0.54 + 0.88 + 0.9)/4 = 0.58
Specificity = 1-false positive rate[P(T=1) > 0.5]
= 1- [(0.78)/6]
= 0.87
Not sure if my working above is correct. I would appreciate if someone can guide me to the correct solution. Thanks.
classification self-study
asked Mar 30 at 16:04
vic12vic12
132
$endgroup$
add a comment |
$begingroup$
I have the following table with predictive probabilities and true class labels:
beginarray
hline
P(T=1) &0.54& 0.23 & 0.78 & 0.88 & 0.26 & 0.41 & 0.90 & 0.45&0.19&0.36 \ hline
T&1&0 &0 &1 &0 &0& 1& 1& 0& 0\ hline
endarray
The question is to compute the specificity & sensitivity at the threshold of 0.5.
My attempt at answering this question:
Sensitivity = true positive rate[P(T=1) > 0.5]
= (0.54 + 0.88 + 0.9)/4 = 0.58
Specificity = 1-false positive rate[P(T=1) > 0.5]
= 1- [(0.78)/6]
= 0.87
Not sure if my working above is correct. I would appreciate if someone can guide me to the correct solution. Thanks.
classification self-study
asked Mar 30 at 16:04
vic12vic12
132
$endgroup$
I have the following table with predictive probabilities and true class labels:
beginarray
hline
P(T=1) &0.54& 0.23 & 0.78 & 0.88 & 0.26 & 0.41 & 0.90 & 0.45&0.19&0.36 \ hline
T&1&0 &0 &1 &0 &0& 1& 1& 0& 0\ hline
endarray
The question is to compute the specificity & sensitivity at the threshold of 0.5.
My attempt at answering this question:
Sensitivity = true positive rate[P(T=1) > 0.5]
= (0.54 + 0.88 + 0.9)/4 = 0.58
Specificity = 1-false positive rate[P(T=1) > 0.5]
= 1- [(0.78)/6]
= 0.87
Not sure if my working above is correct. I would appreciate if someone can guide me to the correct solution. Thanks.
classification self-study
classification self-study
asked Mar 30 at 16:04
vic12vic12
132
asked Mar 30 at 16:04
vic12vic12
132
asked Mar 30 at 16:04
vic12vic12
132
asked Mar 30 at 16:04
vic12vic12
132
asked Mar 30 at 16:04
vic12vic12
132
132
add a comment |
add a comment |
1 Answer
1
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$begingroup$
For threshold = $0.5$ we have:
Sensitivity = True Positive Rate
= (number of points with label $1$ and $P(T = 1)geq 0.5$) divided by (number of points with label $1$)
= $left|(1, 0.54), (1, 0.88), (1, 0.90)right| / 4$ = $3/4$ = $0.75$
Specificity = 1 - False Positive Rate
= 1 - (number of points with label $0$ and $P(T = 1)geq 0.5$) divided by (number of points with label $0$)
= $1 - left|(0, 0.78)right|/6$ = $1 - 1/6$ = $0.833$
answered Mar 30 at 16:47
EsmailianEsmailian
3,156320
$endgroup$
add a comment |
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$begingroup$
For threshold = $0.5$ we have:
Sensitivity = True Positive Rate
= (number of points with label $1$ and $P(T = 1)geq 0.5$) divided by (number of points with label $1$)
= $left|(1, 0.54), (1, 0.88), (1, 0.90)right| / 4$ = $3/4$ = $0.75$
Specificity = 1 - False Positive Rate
= 1 - (number of points with label $0$ and $P(T = 1)geq 0.5$) divided by (number of points with label $0$)
= $1 - left|(0, 0.78)right|/6$ = $1 - 1/6$ = $0.833$
answered Mar 30 at 16:47
EsmailianEsmailian
3,156320
$endgroup$
add a comment |
$begingroup$
For threshold = $0.5$ we have:
Sensitivity = True Positive Rate
= (number of points with label $1$ and $P(T = 1)geq 0.5$) divided by (number of points with label $1$)
= $left|(1, 0.54), (1, 0.88), (1, 0.90)right| / 4$ = $3/4$ = $0.75$
Specificity = 1 - False Positive Rate
= 1 - (number of points with label $0$ and $P(T = 1)geq 0.5$) divided by (number of points with label $0$)
= $1 - left|(0, 0.78)right|/6$ = $1 - 1/6$ = $0.833$
answered Mar 30 at 16:47
EsmailianEsmailian
3,156320
$endgroup$
add a comment |
$begingroup$
For threshold = $0.5$ we have:
Sensitivity = True Positive Rate
= (number of points with label $1$ and $P(T = 1)geq 0.5$) divided by (number of points with label $1$)
= $left|(1, 0.54), (1, 0.88), (1, 0.90)right| / 4$ = $3/4$ = $0.75$
Specificity = 1 - False Positive Rate
= 1 - (number of points with label $0$ and $P(T = 1)geq 0.5$) divided by (number of points with label $0$)
= $1 - left|(0, 0.78)right|/6$ = $1 - 1/6$ = $0.833$
answered Mar 30 at 16:47
EsmailianEsmailian
3,156320
$endgroup$
For threshold = $0.5$ we have:
Sensitivity = True Positive Rate
= (number of points with label $1$ and $P(T = 1)geq 0.5$) divided by (number of points with label $1$)
= $left|(1, 0.54), (1, 0.88), (1, 0.90)right| / 4$ = $3/4$ = $0.75$
Specificity = 1 - False Positive Rate
= 1 - (number of points with label $0$ and $P(T = 1)geq 0.5$) divided by (number of points with label $0$)
= $1 - left|(0, 0.78)right|/6$ = $1 - 1/6$ = $0.833$
answered Mar 30 at 16:47
EsmailianEsmailian
3,156320
edited Mar 30 at 16:53
answered Mar 30 at 16:47
EsmailianEsmailian
3,156320
answered Mar 30 at 16:47
EsmailianEsmailian
3,156320
answered Mar 30 at 16:47
EsmailianEsmailian
3,156320
3,156320
add a comment |
add a comment |
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