Editing Cluster analysis (section)

==== [[F-measure]] ====
The F-measure can be used to balance the contribution of [[false negative]]s by weighting [[recall (information retrieval)|recall]] through a parameter <math>\beta \geq 0</math>. Let '''[[precision (information retrieval)|precision]]''' and '''[[recall (information retrieval)|recall]]''' (both external evaluation measures in themselves) be defined as follows:
<math>
P = \frac {TP } {TP + FP }
</math>
<math>
R = \frac {TP } {TP + FN}
</math>
where <math>P</math> is the [[precision (information retrieval)|precision]] rate and <math>R</math> is the [[recall (information retrieval)|recall]] rate. We can calculate the F-measure by using the following formula:<ref name="Christopher D. Manning, Prabhakar Raghavan & Hinrich Schutze"/>
<math>
F_{\beta} = \frac {(\beta^2 + 1)\cdot P \cdot R } {\beta^2 \cdot P + R}
</math>
When <math>\beta=0</math>, <math>F_{0}=P</math>. In other words, [[recall (information retrieval)|recall]] has no impact on the F-measure when <math>\beta=0</math>, and increasing <math>\beta</math> allocates an increasing amount of weight to recall in the final F-measure.
Also <math>TN</math> is not taken into account and can vary from 0 upward without bound.