# v_measure_score

### cuml.metrics.cluster.v_measure_score(labels_true, labels_pred, beta=1.0) → [float](https://docs.python.org/3/builtins/functions.html#float)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/metrics/cluster/v_measure.pyx#L22)

cython_v_measure(labels_true, labels_pred, beta=1.0) -> float

V-measure metric of a cluster labeling given a ground truth.

The V-measure is the harmonic mean between homogeneity and completeness:

```default
v = (1 + beta) * homogeneity * completeness
     / (beta * homogeneity + completeness)
```

This metric is independent of the absolute values of the labels:
a permutation of the class or cluster label values won’t change the
score value in any way.

This metric is furthermore symmetric: switching `label_true` with
`label_pred` will return the same score value. This can be useful to
measure the agreement of two independent label assignments strategies
on the same dataset when the real ground truth is not known.

* **Parameters:**
  **labels_pred**
  : The labels predicted by the model for the test dataset.
    Acceptable formats: cuDF DataFrame, NumPy ndarray, Numba device
    ndarray, cuda array interface compliant array like CuPy

  **labels_true**
  : The ground truth labels (ints) of the test dataset.
    Acceptable formats: cuDF DataFrame, NumPy ndarray, Numba device
    ndarray, cuda array interface compliant array like CuPy

  **beta**
  : Ratio of weight attributed to `homogeneity` vs `completeness`.
    If `beta` is greater than 1, `completeness` is weighted more
    strongly in the calculation. If `beta` is less than 1,
    `homogeneity` is weighted more strongly.
* **Returns:**
  **v_measure_value**
  : score between 0.0 and 1.0. 1.0 stands for perfectly complete labeling

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