# approximate_predict

### cuml.cluster.hdbscan.approximate_predict(clusterer, points_to_predict)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/cluster/hdbscan/hdbscan.pyx#L1280)

Predict the cluster label of new points. The returned labels
will be those of the original clustering found by `clusterer`,
and therefore are not (necessarily) the cluster labels that would
be found by clustering the original data combined with
`points_to_predict`, hence the ‘approximate’ label.

If you simply wish to assign new points to an existing clustering
in the ‘best’ way possible, this is the function to use. If you
want to predict how `points_to_predict` would cluster with
the original data under HDBSCAN the most efficient existing approach
is to simply recluster with the new point(s) added to the original dataset.

* **Parameters:**
  **clusterer**
  : A clustering object that has been fit to the data and
    had `prediction_data=True` set.

  **points_to_predict**
  : The new data points to predict cluster labels for. They should
    have the same dimensionality as the original dataset over which
    clusterer was fit.
* **Returns:**
  **labels**
  : The predicted labels of the `points_to_predict`

  **probabilities**
  : The soft cluster scores for each of the `points_to_predict`

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