# roc_auc_score

### cuml.metrics.roc_auc_score(y_true, y_score)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/metrics/_ranking.py#L109)

Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC)
from prediction scores.

#### NOTE
this implementation can only be used with binary classification.

* **Parameters:**
  **y_true**
  : True labels. The binary cases
    expect labels with shape (n_samples,)

  **y_score**
  : Target scores. In the binary cases, these can be either
    probability estimates or non-thresholded decision values (as returned
    by `decision_function` on some classifiers). The binary
    case expects a shape (n_samples,), and the scores must be the scores of
    the class with the greater label.
* **Returns:**
  **auc**

### Examples

```pycon
>>> import numpy as np
>>> from cuml.metrics import roc_auc_score
>>> y_true = np.array([0, 0, 1, 1])
>>> y_scores = np.array([0.1, 0.4, 0.35, 0.8])
>>> print(roc_auc_score(y_true, y_scores))
0.75
```

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