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Sensitivity

Innocent Iguana answered on April 23, 2022 Popularity 10/10 Helpfulness 4/10

answer Sensitivity

related sensitivity

Sensitivity

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Sensitivity:
	- True positive rate 
    - Out of all positive values in the dataset, how many values are correctly identified as positive by the model
    - Formula : TP / (TP + FN)
Specificity:
	- True negative rate 
    - Out of all negative values in the dataset, how many values are correctly identified as negative by the model
    - Formula : TN / (TN + FP)

Popularity 10/10 Helpfulness 4/10 Language whatever

Source: Grepper

Tags: whatever

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Contributed on Feb 09 2023

Innocent Iguana

0 Answers Avg Quality 2/10

Closely Related Answers

sensitivity

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FP = confusion_matrix.sum(axis=0) - np.diag(confusion_matrix)  
FN = confusion_matrix.sum(axis=1) - np.diag(confusion_matrix)
TP = np.diag(confusion_matrix)
TN = confusion_matrix.values.sum() - (FP + FN + TP)

# Sensitivity, hit rate, recall, or true positive rate
TPR = TP/(TP+FN)
# Specificity or true negative rate
TNR = TN/(TN+FP) 
# Precision or positive predictive value
PPV = TP/(TP+FP)
# Negative predictive value
NPV = TN/(TN+FN)
# Fall out or false positive rate
FPR = FP/(FP+TN)
# False negative rate
FNR = FN/(TP+FN)
# False discovery rate
FDR = FP/(TP+FP)

# Overall accuracy
ACC = (TP+TN)/(TP+FP+FN+TN)

Popularity 4/10 Helpfulness 1/10 Language python

Source: stackoverflow.com

Tags: python

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