classification performance metric for high risk medical decisions
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What is the best classification performance metric for risky medical treatments like surgery? for example a patient should NOT suggest a surgery (negative) if he/she can be treated by medicine (positive). Does Negative predictive value (TN/TN+FN) works for this situation?
classification predictive-modeling performance
asked Jul 31 '18 at 1:24
Amad ArianAmad Arian
31
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add a comment |
$begingroup$
What is the best classification performance metric for risky medical treatments like surgery? for example a patient should NOT suggest a surgery (negative) if he/she can be treated by medicine (positive). Does Negative predictive value (TN/TN+FN) works for this situation?
classification predictive-modeling performance
asked Jul 31 '18 at 1:24
Amad ArianAmad Arian
31
$endgroup$
add a comment |
$begingroup$
What is the best classification performance metric for risky medical treatments like surgery? for example a patient should NOT suggest a surgery (negative) if he/she can be treated by medicine (positive). Does Negative predictive value (TN/TN+FN) works for this situation?
classification predictive-modeling performance
asked Jul 31 '18 at 1:24
Amad ArianAmad Arian
31
$endgroup$
What is the best classification performance metric for risky medical treatments like surgery? for example a patient should NOT suggest a surgery (negative) if he/she can be treated by medicine (positive). Does Negative predictive value (TN/TN+FN) works for this situation?
classification predictive-modeling performance
classification predictive-modeling performance
asked Jul 31 '18 at 1:24
Amad ArianAmad Arian
31
asked Jul 31 '18 at 1:24
Amad ArianAmad Arian
31
asked Jul 31 '18 at 1:24
Amad ArianAmad Arian
31
asked Jul 31 '18 at 1:24
Amad ArianAmad Arian
31
asked Jul 31 '18 at 1:24
Amad ArianAmad Arian
31
31
add a comment |
add a comment |
3 Answers
3
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oldest
votes
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- Minimizing false negatives is definitely a good strategy.
- You can also generalize to using weighted f-measure. It allows you to give tunable weightage.
answered Jul 31 '18 at 3:20
hssayhssay
1,0931311
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add a comment |
$begingroup$
This could be the concept you are looking for:
Cost curves.
The concept is ROC curve but with cost associated for every type of cost.
For example: False negatives have a cost of 100. False positives have a cost of 5. Using cost-associated ROC curves will help you punishing much more FN than FP or viceversa.
edited yesterday
Stephen Rauch♦
1,52551330
answered yesterday
Juan Esteban de la CalleJuan Esteban de la Calle
687
New contributor
Juan Esteban de la Calle is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
Check ROC curve, increase the threshold and measure PPV.
Also you can not use only one in isolation, you have to check sensitivity, specificity and PPV in order to understand the complete scenario.
answered yesterday
Cini09Cini09
166
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3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
- Minimizing false negatives is definitely a good strategy.
- You can also generalize to using weighted f-measure. It allows you to give tunable weightage.
answered Jul 31 '18 at 3:20
hssayhssay
1,0931311
$endgroup$
add a comment |
$begingroup$
- Minimizing false negatives is definitely a good strategy.
- You can also generalize to using weighted f-measure. It allows you to give tunable weightage.
answered Jul 31 '18 at 3:20
hssayhssay
1,0931311
$endgroup$
add a comment |
$begingroup$
- Minimizing false negatives is definitely a good strategy.
- You can also generalize to using weighted f-measure. It allows you to give tunable weightage.
answered Jul 31 '18 at 3:20
hssayhssay
1,0931311
$endgroup$
- Minimizing false negatives is definitely a good strategy.
- You can also generalize to using weighted f-measure. It allows you to give tunable weightage.
answered Jul 31 '18 at 3:20
hssayhssay
1,0931311
answered Jul 31 '18 at 3:20
hssayhssay
1,0931311
answered Jul 31 '18 at 3:20
hssayhssay
1,0931311
answered Jul 31 '18 at 3:20
hssayhssay
1,0931311
1,0931311
add a comment |
add a comment |
$begingroup$
This could be the concept you are looking for:
Cost curves.
The concept is ROC curve but with cost associated for every type of cost.
For example: False negatives have a cost of 100. False positives have a cost of 5. Using cost-associated ROC curves will help you punishing much more FN than FP or viceversa.
edited yesterday
Stephen Rauch♦
1,52551330
answered yesterday
Juan Esteban de la CalleJuan Esteban de la Calle
687
New contributor
Juan Esteban de la Calle is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
This could be the concept you are looking for:
Cost curves.
The concept is ROC curve but with cost associated for every type of cost.
For example: False negatives have a cost of 100. False positives have a cost of 5. Using cost-associated ROC curves will help you punishing much more FN than FP or viceversa.
edited yesterday
Stephen Rauch♦
1,52551330
answered yesterday
Juan Esteban de la CalleJuan Esteban de la Calle
687
New contributor
Juan Esteban de la Calle is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
This could be the concept you are looking for:
Cost curves.
The concept is ROC curve but with cost associated for every type of cost.
For example: False negatives have a cost of 100. False positives have a cost of 5. Using cost-associated ROC curves will help you punishing much more FN than FP or viceversa.
edited yesterday
Stephen Rauch♦
1,52551330
answered yesterday
Juan Esteban de la CalleJuan Esteban de la Calle
687
New contributor
Juan Esteban de la Calle is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
This could be the concept you are looking for:
Cost curves.
The concept is ROC curve but with cost associated for every type of cost.
For example: False negatives have a cost of 100. False positives have a cost of 5. Using cost-associated ROC curves will help you punishing much more FN than FP or viceversa.
edited yesterday
Stephen Rauch♦
1,52551330
answered yesterday
Juan Esteban de la CalleJuan Esteban de la Calle
687
New contributor
Juan Esteban de la Calle is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited yesterday
Stephen Rauch♦
1,52551330
edited yesterday
Stephen Rauch♦
1,52551330
edited yesterday
Stephen Rauch♦
1,52551330
1,52551330
answered yesterday
Juan Esteban de la CalleJuan Esteban de la Calle
687
New contributor
Juan Esteban de la Calle is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered yesterday
Juan Esteban de la CalleJuan Esteban de la Calle
687
answered yesterday
Juan Esteban de la CalleJuan Esteban de la Calle
687
687
New contributor
Juan Esteban de la Calle is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Juan Esteban de la Calle is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Juan Esteban de la Calle is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
$begingroup$
Check ROC curve, increase the threshold and measure PPV.
Also you can not use only one in isolation, you have to check sensitivity, specificity and PPV in order to understand the complete scenario.
answered yesterday
Cini09Cini09
166
$endgroup$
add a comment |
$begingroup$
Check ROC curve, increase the threshold and measure PPV.
Also you can not use only one in isolation, you have to check sensitivity, specificity and PPV in order to understand the complete scenario.
answered yesterday
Cini09Cini09
166
$endgroup$
add a comment |
$begingroup$
Check ROC curve, increase the threshold and measure PPV.
Also you can not use only one in isolation, you have to check sensitivity, specificity and PPV in order to understand the complete scenario.
answered yesterday
Cini09Cini09
166
$endgroup$
Check ROC curve, increase the threshold and measure PPV.
Also you can not use only one in isolation, you have to check sensitivity, specificity and PPV in order to understand the complete scenario.
answered yesterday
Cini09Cini09
166
answered yesterday
Cini09Cini09
166
answered yesterday
Cini09Cini09
166
answered yesterday
Cini09Cini09
166
166
add a comment |
add a comment |
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