
TERMS 
 True Positive = correct estimation : Actual TRUE condition is correctly estimated as TRUE by algorithm.
 True Negative = correct estimation : Actual FALSE condition is correctly estimated as FALSE by algorithm.
 False Positive = incorrect estimation : Actual FALSE condition is incorrectly estimated as TRUE by algorithm.
 False Negative = incorrect estimation : Actual TRUE condition is incorrectly estimated as FALSE by algorithm.

 Accuracy = ability to estimate correctly in actual BOTH conditions.
 Sensitivity = ability to estimate correctly in actual TRUE condition.
 False Negative Rate = possibility of Actual TRUE samples which are unable to find.
 Specificity = ability to estimate correctly in actual FALSE condition.
 False Positive Rate = possibility of Actual FALSE samples which are unable to find.
 Positive Predictability = confidence of positive estimation.
 Negative Predictability = confidence of negative estimation.

DEFINITIONS 
 Accuracy = ( True Positive + True Negative ) / Number of all samples
 Sensitivity = ( True Positive ) / Number of Actual TRUE samples
 False Negative Rate = ( Unfound Number of Actual TRUE samples ) / Number of Actual TRUE samples
 Specificity = ( True Negative ) / Number of Actual FALSE samples
 False Positive Rate = ( Unfound Number of Actual FALSE samples ) / Number of Actual FALSE samples
 Positive Predictability = ( True Positive ) / ( True Positive + False Positive )
 Negative Predictability = ( True Negative ) / ( True Negative + False Negative )

 False Negative Rate + Sensitivity = 1
 False Positive Rate + Specificity = 1

DIAGRAMMATIC VIEW (rewrite from Wikipedia) 

Actual Condition 

True 
False 
Algorithmic Estimation 
Positive 
True Positive 
False Positive 
Positive Predictability 
Negative 
False Negative 
True Negative 
Negative Predictability 

Sensitivity 
Specificity 
Accuracy 

REFERENCES 
