'Multiclass classification per class recall equals per class accuracy?
I've got a multiclass problem. I'm using sklearn.metrics to calculate the confusion matrix, overall accuracy, per class precision, per class recall and per class F1-score.
Now I wanted to calculate the per class accuracy. Since there is no method in sklearn for this I used another one which i got from a google search. I've now realised, that the per class recall equals the per class accuracy. Can anyone explain to me if this holds true and if yes, why?
I found an explanation here, but I'm not sure since there the micro-recall equals the overall accuracy if I'm understanding it correctly. And I'm looking for the per class accuracy.
Solution 1:[1]
This generally doesn't hold. Accuracy and recall are calculated using different formulas and are different measures explaining something else.
Recall is the percentage of true positive data points compared to all data points that are predicted as positive by your classifier.
Accuracy is the percentage of all examples that are classified correctly, including positive and negative.
If they are equal, this is either coincidence or you have an error is your method of calculating them. Most likely this will be coincidence.
Sources
This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.
Source: Stack Overflow
| Solution | Source |
|---|---|
| Solution 1 | Ethan Van den Bleeken |