# tf.contrib.metrics.auc_using_histogram
|---|
| [ View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/metrics/python/ops/histogram_ops.py#L39-L94) |
AUC computed by maintaining histograms.
tf.contrib.metrics.auc_using_histogram(
boolean_labels, scores, score_range, nbins=100, collections=None,
check_shape=True, name=None
)
Rather than computing AUC directly, this Op maintains Variables containing
histograms of the scores associated with `True` and `False` labels. By
comparing these the AUC is generated, with some discretization error.
See: "Efficient AUC Learning Curve Calculation" by Bouckaert.
This AUC Op updates in `O(batch_size + nbins)` time and works well even with
large class imbalance. The accuracy is limited by discretization error due
to finite number of bins. If scores are concentrated in a fewer bins,
accuracy is lower. If this is a concern, we recommend trying different
numbers of bins and comparing results.
| ## Args ||
|---|---|
| `boolean_labels` | 1-D boolean `Tensor`. Entry is `True` if the corresponding record is in class. |
| `scores` | 1-D numeric `Tensor`, same shape as boolean_labels. |
| `score_range` | `Tensor` of shape `[2]`, same dtype as `scores`. The min/max values of score that we expect. Scores outside range will be clipped. |
| `nbins` | Integer number of bins to use. Accuracy strictly increases as the number of bins increases. |
| `collections` | List of graph collections keys. Internal histogram Variables are added to these collections. Defaults to `[GraphKeys.LOCAL_VARIABLES]`. |
| `check_shape` | Boolean. If `True`, do a runtime shape check on the scores and labels. |
| `name` | A name for this Op. Defaults to "auc_using_histogram". |
| ## Returns ||
|---|---|
| `auc` | `float32` scalar `Tensor`. Fetching this converts internal histograms to auc value. |
| `update_op` | `Op`, when run, updates internal histograms. |