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Balanced accuracy is the averaged accuracy for each class, e.g. (positive_class_accuracy + negative_class_accuracy) / 2.
This parameter is important for imbalanced datasets, which have significantly different number of samples in different classes.
If a classifier has a similar performance for both negative and positive classes, accuracy and balanced accuracy are also similar.
BACC BA = 0.5 * (TP / (TP + FN) + TN / (TN + FP)) = 0.5 * (SENS + SPEC)
Matthews correlation coefficient (MCC)
MCC takes into account true and false positives and negatives and is generally regarded as a balanced measure which can be used even if the classes are of very different sizes.
MCC = (TP*TN - FP FN)/ SQRT( (TP+FP)(TP+FN)(TN+FP)(TN+FN) )
Area under the curve (AUC)
Receiver Operating Characteristic AUC (ROC-AUC) is calculated on the plot of Sensitivity vs Specificity, which is shown for each classification model
Confusion matrix
Confusion matrix shows the number of samples from a particular class classified as another particular class.
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