V Measure#
V Measure is an entropy-based clustering metric that balances Homogeneity and Completeness. It has the additional property of being symmetric in that the predictions and ground-truth can be swapped without changing the score.
Estimator Compatibility: Clusterer
Output Range: 0 to 1
Parameters#
# | Name | Default | Type | Description |
---|---|---|---|---|
1 | beta | 1.0 | float | The ratio of weight given to homogeneity over completeness. |
Example#
use Rubix\ML\CrossValidation\Metrics\VMeasure;
$metric = new VMeasure(1.0);
References#
-
A. Rosenberg et al. (2007). V-Measure: A conditional entropy-based external cluster evaluation measure. ↩
Last update:
2021-03-03