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tn, fp, fn, tp = confusion_matrix(list(y_true), list(y_pred), labels=[0, 1]).ravel()
print('True Positive', tp)
print('True Negative', tn)
print('False Positive', fp)
print('False Negative', fn)
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def perf_measure(y_actual, y_hat):
TP = 0
FP = 0
TN = 0
FN = 0
for i in range(len(y_hat)):
if y_actual[i]==y_hat[i]==1:
TP += 1
if y_hat[i]==1 and y_actual[i]!=y_hat[i]:
FP += 1
if y_actual[i]==y_hat[i]==0:
TN += 1
if y_hat[i]==0 and y_actual[i]!=y_hat[i]:
FN += 1
return(TP, FP, TN, FN)