• General
• Histogram

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

F1-Score

The F1-Score is the harmonic mean of precision and recall: \[ F_1 = 2 \cdot \frac{precision \cdot recall}{precision + recall} \] Where Precision is defined as \[ \frac{TP}{TP+FP} \] and Recall is defined as \[ \frac{TP}{TP+FN} \] The F1-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