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

I have tried some of the answers and found them not working.

This works for me:

from sklearn.metrics import classification_report

print(classification_report(y_test, predicted)) 

Answer 2

If you have two lists that have the predicted and actual values; as it appears you do, you can pass them to a function that will calculate TP, FP, TN, FN with something like this:

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)

From here I think you will be able to calculate rates of interest to you, and other performance measure like specificity and sensitivity.

Answer 3

According to scikit-learn documentation,

http://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix

By definition a confusion matrix C is such that C[i, j] is equal to the number of observations known to be in group i but predicted to be in group j.

Thus in binary classification, the count of true negatives is C[0,0], false negatives is C[1,0], true positives is C[1,1] and false positives is C[0,1].

CM = confusion_matrix(y_true, y_pred)

TN = CM[0][0]
FN = CM[1][0]
TP = CM[1][1]
FP = CM[0][1]

Answer 4

#False positive cases
train = pd.merge(X_train, y_train,left_index=True, right_index=True)
y_train_pred = pd.DataFrame(y_train_pred)
y_train_pred.rename(columns={0 :'Predicted'}, inplace=True )
train = train.reset_index(drop=True).merge(y_train_pred.reset_index(drop=True),
left_index=True,right_index=True)
train['FP'] = np.where((train['Banknote']=="Forged") & (train['Predicted']=="Genuine"),1,0)
train[train.FP != 0]

Answer 5

if you have more than one classes in your classifier, you might want to use pandas-ml at that part. Confusion Matrix of pandas-ml give more detailed information. check that

Answer 6

The one liner to get true postives etc. out of the confusion matrix is to ravel it:

from sklearn.metrics import confusion_matrix

y_true = [1, 1, 0, 0]
y_pred = [1, 0, 1, 0]   

tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
print(tn, fp, fn, tp)  # 1 1 1 1