ClustEval
clustering evaluation framework
Hints:
On the right side you see an explanation and more details regarding the clustering quality measure.
False Positive Rate
The FPR relates the number of false positives to the total number of negative elements. This estimates a likelihood of an element being predicted positive, if it is negative. This measure is based on the pairwise approach to calculate TP,TN,FP and FN. \[ FPR = \frac{FP}{FP+TN} \] In the binary classification background we have two classes that we want to distinguish: positive and negative. In this scenario there are four possible outcomes:- TP (True Positive): The object belongs to class positive and we classified it as positive,
- FP (False Positive ): The object belongs to class negative and we classified it as positive,
- TN (True Negative): The object belongs to class negative and we classified it as negative,
- FN (False Negative): The object belongs to class positive but we classified it as negative
| Reality | |||
| Positive | Negative | ||
| Prediction | Positive | TP | FP |
| Negative | FN | TN | |