Difference between model accuracy from test data and confusion matrix accuracy

Question

• I am working for NLP project where I wanted to do text classification using neural n/w
• I am getting very nice accuracy from the test set as 98%.
• But, when I tried to check the confusion matrix accuracy (the accuracy score using confusion matrix) it's just 52%.
• How is it possible? What am I missing here?

Question

What is the difference between both the accuracy's which one should be considered as the actual accuracy? and why?

Code on test set

loss, acc = model.evaluate(Xtest, y_test_array)

Answer 1

It looks like your dataset has class imbalance, and the metric calculated from confusion matrix (it is NOT accuracy - probably, it is something like F1 score) is low because the minority class is recognized poorly. At the same time, accuracy is high because the majority class is recognized well.