ClustEval clustering evaluation framework
Hints:

On the right side you see an explanation and more details regarding the clustering quality measure.

F2-Score

The F2-Score is a weighted average of precision and recall: $F_2 = 5 \cdot \frac{precision \cdot recall}{4\cdot precision + recall}$ Where Precision is defined as $\frac{TP}{TP+FP}$ and Recall is defined as $\frac{TP}{TP+FN}$ The F2-Score originates from the binary classification background, where we only 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