How do you find the sensitivity and specificity of a confusion matrix?
Sensitivity (SN) is calculated as the number of correct positive predictions divided by the total number of positives. It is also called recall (REC) or true positive rate (TPR). The best sensitivity is 1.0, whereas the worst is 0.0.
What is sensitivity and specificity in R?
The sensitivity is defined as the proportion of positive results out of the number of samples which were actually positive. Similarly, when there are no negative results, specificity is not defined and a value of NA is returned. Similar statements are true for predictive values.
How is sensitivity and specificity measured in caret?
As shown below, another function called twoClassSummary can be used to get the sensitivity and specificity using the default probability cutoff. Another function, multiClassSummary, can do similar calculations when there are three or more classes but both require class probabilities for each class.
Which is the default metric in caret-PPV?
The default metric printed is accuracy and Cohen kappa. I want to extract the matching metrics like sensitivity, specificity, positive predictive value etc. but I cannot find an easy way to do it. The final model is provided but it is trained on all the data (as far as I can tell from documentation), so I cannot use it for predicting anew.
What is the function for two classes in caret?
For two classes, this function assumes that the class corresponding to an event is the first class level (but this can be changed using the positive argument. Note that there are a number of statistics shown here.
How is sensitivity calculated in a three class problem?
For example, in a three class problem, the sensitivity of the first class is calculated against all the samples in the second and third classes (and so on). The confusionMatrix matrix frames the errors in terms of sensitivity and specificity. In the case of information retrieval, the precision and recall might be more appropriate.