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

**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 it - in order to be able to work with it.

Once I have done that I start the classification:

- I use the
`kfold`

cross validation method in order to obtain the mean accuracy and train a classifier. - I make the predictions and obtain the accuracy & confusion matrix of that fold.
- After this, I would like to obtain the
`True Positive(TP)`

,`True Negative(TN)`

,`False Positive(FP)`

and`False Negative(FN)`

values. I'll use these parameters to obtain the**Sensitivity**and**Specificity**.

Finally, I would use this to put in HTML in order to show a chart with the TPs of each label.

**Code:**

The variables I have for the moment:

```
trainList #It is a list with all the data of my dataset in JSON form
labelList #It is a list with all the labels of my data
```

Most part of the method:

```
#I transform the data from JSON form to a numerical one
X=vec.fit_transform(trainList)
#I scale the matrix (don't know why but without it, it makes an error)
X=preprocessing.scale(X.toarray())
#I generate a KFold in order to make cross validation
kf = KFold(len(X), n_folds=10, indices=True, shuffle=True, random_state=1)
#I start the cross validation
for train_indices, test_indices in kf:
X_train=[X[ii] for ii in train_indices]
X_test=[X[ii] for ii in test_indices]
y_train=[listaLabels[ii] for ii in train_indices]
y_test=[listaLabels[ii] for ii in test_indices]
#I train the classifier
trained=qda.fit(X_train,y_train)
#I make the predictions
predicted=qda.predict(X_test)
#I obtain the accuracy of this fold
ac=accuracy_score(predicted,y_test)
#I obtain the confusion matrix
cm=confusion_matrix(y_test, predicted)
#I should calculate the TP,TN, FP and FN
#I don't know how to continue
```

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.

From: stackoverflow.com/q/31324218