EvaluationMeasures.TNR {EvaluationMeasures} | R Documentation |
TNR of prediction
EvaluationMeasures.TNR(Real = NULL, Predicted = NULL, Positive = 1,
TP = NULL, TN = NULL, FP = NULL, FN = NULL)
Real |
Real binary values of the class |
Predicted |
Predicted binary values of the class |
Positive |
Consider 1 label as Positive Class unless changing this parameter to 0 |
TP |
Number of True Positives. Number of 1 in real which is 1 in predicted. |
TN |
Number of True Negatives. Number of 0 in real which is 0 in predicted. |
FP |
Number of False Positives. Number of 0 in real which is 1 in predicted. |
FN |
Number of False Negatives. Number of 1 in real which is 0 in predicted. |
True Negative Rate is Proportional of negatives that are correctly identified
By getting the predicted and real values or number of TP,TN,FP,FN return the Specificity or True Negative Rate of model
TNR
Babak Khorsand
EvaluationMeasures.TNR(c(1,0,1,0,1,0,1,0),c(1,1,1,1,1,1,0,0))