The best way to calculate classification accuracy?
Classification accuracy is defined as "percentage of correct predictions". That is the case regardless of the number of classes. Thus, scenario 1 has a higher classification accuracy than scenario 2.
However, it sounds like what you are really asking is for an alternative evaluation metric or process that "rewards" scenario 2 for only making certain types of mistakes. I have two suggestions:
- Create a confusion matrix: It describes the performance of a classifier so that you can see what types of errors your classifier is making.
- Calculate the precision, recall, and F1 score for each class. The average F1 score might be the single-number metric you are looking for.
The Classification metrics section of the scikit-learn documentation has lots of good information about classifier evaluation, even if you are not a scikit-learn user.
SimpleDreamful
Updated on June 04, 2022Comments
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SimpleDreamful almost 2 years
I know one formula to calculate classification accuracy is X = t / n * 100 (where t is the number of correct classification and n is the total number of samples. )
But, let's say we have total 100 samples, 80 in class A, 10 in class B, 10 in class C.
Scenario 1: All 100 samples were assigned to class A, by using the formula, we got accuracy equals 80%.
Scenario 2: 10 samples belong to B were correctly assigned to class B ;10 samples belong to C were correctly assigned to class C as well; 30 samples belong to A correctly assigned to class A; the rest 50 samples belong to A were incorrectly assigned to C. By using the formula, we got accuracy of 50%.
My question is:
1: Can we say scenario 1 has a higher accuracy rate then scenario 2?
2: Is there any way to calculate accuracy rate for classification problem?
Many thanks ahead!
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WestCoastProjects about 8 yearsConcise but "information rich" answer. Give yourself a Shannon pat.