Description Usage Arguments Details Value Author(s) Examples

Calculate the specificity for a given logit model.

1 | ```
specificity(actuals, predictedScores, threshold = 0.5)
``` |

`actuals` |
The actual binary flags for the response variable. It can take a numeric vector containing values of either 1 or 0, where 1 represents the 'Good' or 'Events' while 0 represents 'Bad' or 'Non-Events'. |

`predictedScores` |
The prediction probability scores for each observation. If your classification model gives the 1/0 predcitions, convert it to a numeric vector of 1's and 0's. |

`threshold` |
If predicted value is above the threshold, it will be considered as an event (1), else it will be a non-event (0). Defaults to 0.5. |

For a given given binary response actuals and predicted probability scores, specificity is defined as number of observations without the event AND predicted to not have the event divided by the number of observations without the event. Specificity is particularly useful when you are extra careful not to predict a non event as an event, like in spam detection where you dont want to classify a genuine mail as spam(event) where it may be somewhat ok to occasionally classify a spam as a genuine mail(a non-event).

The specificity of the given binary response actuals and predicted probability scores, which is, the number of observations without the event AND predicted to not have the event divided by the nummber of observations without the event.

Selva Prabhakaran [email protected]

1 2 | ```
data('ActualsAndScores')
specificity(actuals=ActualsAndScores$Actuals, predictedScores=ActualsAndScores$PredictedScores)
``` |

InformationValue documentation built on May 29, 2017, 11:57 a.m.

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