EvaluationMeasures.TNR: EvaluationMeasures.TNR

In EvaluationMeasures: Collection of Model Evaluation Measure Functions

Description

TNR of prediction

Usage

EvaluationMeasures.TNR(Real = NULL, Predicted = NULL, Positive = 1,
  TP = NULL, TN = NULL, FP = NULL, FN = NULL)

Arguments

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.

Details

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

Value

TNR

Author(s)

Babak Khorsand

Examples

EvaluationMeasures.TNR(c(1,0,1,0,1,0,1,0),c(1,1,1,1,1,1,0,0))

EvaluationMeasures documentation built on May 2, 2019, 2:12 p.m.