如何计算误报率 (FPR) 和误报率百分比？

Question

I compute the False positive rate and False negative rate. I am using these techniques:

cnf_matrix=confusion_matrix(Ytest, y_pred)

print(cnf_matrix)

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

FP = FP.astype(float)
print('FP: '+str(FP))
FN = FN.astype(float)
print('Fn: '+str(FN))
TP = TP.astype(float)
print('FN: '+str(FN))
TN = TN.astype(float)
print('TN: '+str(TN))
# false positive rate
FPR = FP/(FP+TN)
print('FPR: '+str(FPR))
# False negative rate
FNR = FN/(TP+FN)
print('FNR: '+str(FNR))

I got these vectors:

FPR: [0.         0.01666667 0.        ]
FNR: [0.         0.         0.03448276]

However, I need to get just one value, not a vector. How to get that?

Answer 1

Your code seems correct, for multiclass classification. The vectors simply give the FPR and FNR for all three classes. Because there will be different FPR and FNR for each class . If you are just interested in FPR/FNR of one class, then you can simply access that score by giving the index

print('FNR: '+str(FNR[0]))   #FNR for 1st class will be at index 0

On the other hand, for binary classification, I think it is better to use scikit-learn's functions to calculate these values.

• FPR = 1 - TNR and TNR = specificity

• FNR = 1 - TPR and TPR = recall

Then, you can calculate FPR and FNR as below:

from sklearn.metrics import recall_score
tpr = recall_score(Ytest, y_pred)   # it is better to name it y_test 
# to calculate, tnr we need to set the positive label to the other class
# I assume your negative class consists of 0, if it is -1, change 0 below to that value
tnr = recall_score(Ytest, y_pred, pos_label = 0) 
fpr = 1 - tnr
fnr = 1 - tpr