education
    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