Scikit-learn: How to obtain True Positive, True Negative, False Positive and False Negative

Question

My problem:

I have a dataset which is a large JSON file. I read it and store it in the trainList variable.

Next, I pre-process

Answer 1

For the multi-class case, everything you need can be found from the confusion matrix. For example, if your confusion matrix looks like this:

Then what you're looking for, per class, can be found like this:

Using pandas/numpy, you can do this for all classes at once like so:

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)