EvaluationMeasures.Sensitivity: EvaluationMeasures.Sensitivity

Description Usage Arguments Details Value Author(s) Examples

View source: R/Sensitivity.R

Description

Sensitivity of prediction

Usage

1
2
EvaluationMeasures.Sensitivity(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

Sensitivity is Proportional of positives that are correctly identified

By getting the predicted and real values or number of TP,TN,FP,FN return the Sensitivity or Recall or True Positive Rate of model

Value

Sensitivity

Author(s)

Babak Khorsand

Examples

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

EvaluationMeasures documentation built on May 30, 2017, 5:22 a.m.

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