How can I print the confusion matrix in the deep learning model?

Question

 def inspection_performance(predicted_fraud, test_fraud): Inspect_Rate = [] Precision=[] Recall=[] for i in range(1,100,1): threshold = np.percentile(predicted_fraud, i) precision = np.mean(test_fraud[predicted_fraud > threshold]) recall = sum(test_fraud[predicted_fraud > threshold])/sum(test_fraud) Inspect_Rate.append(100-i) Precision.append(precision) Recall.append(recall) compiled_conf_matrix = pd.DataFrame({ 'İnceleme Oranı':Inspect_Rate, 'Kesinlik':Precision, 'Hatırlama':Recall, }) return compiled_conf_matrix

I can not print the confusion_matrix results. Then I want to get the certainty, recall, and the f-score, but I can't. This is my code guide. How can I get this by writing code similar to the one below?

from sklearn.metrics import classification_report, confusion_matrix
print(confusion_matrix(y_test, y_pred))

As an example, I want to get results like this at the end.

print(classification_report(y_test, y_pred))

                 precision   recall  f1-score   support

           0       0.96      0.68      0.80     37117
           1       0.14      0.67      0.23      2883

    accuracy                           0.68     40000
   macro avg       0.55      0.68      0.52     40000
weighted avg       0.90      0.68      0.76     40000

I have to write something else instead of y_test and y_pred. But I don't know how to write.

• For reference, the code of the ROC-AUC curve is as follows. Which tag should be instead of (y_true, y_pred) here?

   from matplotlib import pyplot as plt from sklearn.metrics import roc_curve from sklearn.metrics import auc fpr, tpr, _ = roc_curve(test_labels.drop(np.where(np.isnan(y_pred))[0]), np.delete(y_pred, np.where(np.isnan(y_pred))[0])) plt.plot(fpr, tpr, label='ROC curve') plt.plot([0, 1], [0, 1], 'k--', label='Random guess') plt.xlabel('False positive rate') plt.ylabel('True positive rate') plt.title('ROC curve') plt.legend(loc="lower right") plt.show() print('auc: ', auc(fpr, tpr))

• print(confusion_matrix(?, ?))

• print(classification_report(?, ?))

What should get where there are essentially question marks?

Answer 1

Your question is vague. Do you want the confusion matrix or the classification report?

1. For confusion matrix , please refer to this official documentation of confusion matrix . Here you would do something like this:

from sklearn.metrics import confusion_matrix
print(confusion_matrix(y_true, y_pred))

where,

• y_true: ground_truth labels

• y_pred: predicted labels

Now, in your case there are two parameters of the function: predicted_fraud, test_fraud. Is test_fraud your ground_truth? There must be two labels ie either fraud or no fraud (1,0) if yes then,

from sklearn.metrics import confusion_matrix
print(confusion_matrix(test_fraud.classes, predicted_fraud))

2. For the classification report please refer to the official documentation

from sklearn.metrics import classification_report
target_names = ['fraud', 'No fraud']
print(classification_report(test_fraud.classes, predicted_fraud, target_names=target_names))

Classification report will give you the main classification metrics of each class (fraud, no fraud) like: precision, recall, f1 score, accuracy etc.

Furthermore, there is a github link , it helped me too, hope this helps you as well.

Answer 2

I solved the problem. The thing that should happen is as follows.

from sklearn.metrics import confusion_matrix
print(confusion_matrix(test_labels, y_pred.round()))