(Apr-18-2023, 03:53 PM)MrSonoa Wrote: hi, I have tried my best to output the confusion matrix. but it didn't work either. I don't know what coding can produce the output for the confusion matrix.
def print_confusion_matrix(y_true, y_pred): cm = confusion_matrix(y_true, y_pred) print('True positive = ', cm[0][0]) print('False positive = ', cm[0][1]) print('False negative = ', cm[1][0]) print('True negative = ', cm[1][1]) print('\n') df_cm = pd.DataFrame(cm, range(2), range(2)) sn.set(font_scale=1.4) # for label size sn.heatmap(df_cm, annot=True, annot_kws={"size": 16}) # font size plt.ylabel('Actual label', size = 20) plt.xlabel('Predicted label', size = 20) plt.xticks(np.arange(2), ['Fake', 'Real'], size = 16) plt.yticks(np.arange(2), ['Fake', 'Real'], size = 16) plt.ylim([2, 0]) plt.show() predict_x=model.predict(X) classes_x=np.argmax(predict_x,axis=1) print(confusion_matrix)the output:
I just want my coding show from the attachment but don't get it. Any help I will appreciate. Thanks
Error:<function confusion_matrix at 0x7fb8e9bccca0>
It looks like you're trying to print the function itself rather than the result of calling the function. Here's how you can call the
print_confusion_matrix
function to print the confusion matrix:predict_x = model.predict(X) classes_x = np.argmax(predict_x, axis=1) print_confusion_matrix(y_test, classes_x)Make sure to replace
y_test
with your actual test labels. The classes_x
variable should contain the predicted class labels.Also, make sure that you have imported the necessary libraries at the beginning of your code:
from sklearn.metrics import confusion_matrix import pandas as pd import seaborn as sn import matplotlib.pyplot as plt import numpy as npThis code assumes that you have already trained your model and split your data into training and testing sets. If you haven't done so, you'll need to do that first before calling
predict
on your model.I hope this helps! Let me know if you have any further questions.