metrics {mAPKL} | R Documentation |
This function calculates several classification related metrics. It uses the original and the predicted samples' labels to quantify the quality of the classification process. Those meassures give us a direct outlook of the selected "genes" and how well discriminate between two phenotypes.
metrics(classLbls, predLbls)
classLbls |
The initial class labels. |
predLbls |
The predicted class labels. |
AUC |
The Area Under the ROC curve as a degree of samples discrimination |
Accuracy |
The classification accuracy |
MCC |
The MCC classification meassure |
Specificity |
The degree of true negative's identification |
Sensitivity |
The degree of true positive's identification |
Argiris Sakellariou
## Suppose 'val' represent the correct validation set labels ## and 'predictions' the predicted labels according to an SVM model val <- c(rep(0,8),rep(1,4)) predictions <- c(rep(0,6),1,1,rep(1,3),0) perfMetrics <- metrics(classLbls=val, predLbls=predictions)