{ }
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Computes the approximate AUC via a Riemann sum. (deprecated)
tf.compat.v1.metrics.auc(
labels,
predictions,
weights=None,
num_thresholds=200,
metrics_collections=None,
updates_collections=None,
curve='ROC',
name=None,
summation_method='trapezoidal',
thresholds=None
)
The auc
function creates four local variables, true_positives
,
true_negatives
, false_positives
and false_negatives
that are used to
compute the AUC. To discretize the AUC curve, a linearly spaced set of
thresholds is used to compute pairs of recall and precision values. The area
under the ROC-curve is therefore computed using the height of the recall
values by the false positive rate, while the area under the PR-curve is the
computed using the height of the precision values by the recall.
This value is ultimately returned as auc
, an idempotent operation that
computes the area under a discretized curve of precision versus recall values
(computed using the aforementioned variables). The num_thresholds
variable
controls the degree of discretization with larger numbers of thresholds more
closely approximating the true AUC. The quality of the approximation may vary
dramatically depending on num_thresholds
.
For best results, predictions
should be distributed approximately uniformly
in the range [0, 1] and not peaked around 0 or 1. The quality of the AUC
approximation may be poor if this is not the case. Setting summation_method
to 'minoring' or 'majoring' can help quantify the error in the approximation
by providing lower or upper bound estimate of the AUC. The thresholds
parameter can be used to manually specify thresholds which split the
predictions more evenly.
For estimation of the metric over a stream of data, the function creates an
update_op
operation that updates these variables and returns the auc
.
If weights
is None
, weights default to 1. Use weights of 0 to mask values.