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Average Accuracy refers to the percentage of correct predictions made by the model when compared with the actual classifications in the test data. The formula to calculate the Average Accuracy is:
Average Accuracy = (TP/(TP+FP)+TN/(FN+TN))/Number of classes*100
Where:
TP is True Positive.
TN is True Negative.
FP is False Positive.
FN is False Negative.
The average per-class accuracy achieved at a specific probability threshold that is greater than the accuracy achieved at all other possible thresholds.