|TensorFlow 1 version||View source on GitHub|
Computes the approximate AUC (Area under the curve) via a Riemann sum.
See Migration guide for more details.
tf.keras.metrics.AUC( num_thresholds=200, curve='ROC', summation_method='interpolation', name=None, dtype=None, thresholds=None, multi_label=False, label_weights=None )
Used in the notebooks
|Used in the tutorials|
This metric creates four local variables,
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
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
thresholds parameter can be
used to manually specify thresholds which split the predictions more evenly.
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
to 'minoring' or 'majoring' can help quantify the error in the approximation
by providing lower or upper bound estimate of the AUC.
None, weights default to 1.
sample_weight of 0 to mask values.
||(Optional) Defaults to 200. The number of thresholds to use when discretizing the roc curve. Values must be > 1.|
||(Optional) Specifies the name of the curve to be computed, 'ROC' [default] or 'PR' for the Precision-Recall-curve.|
||(Optional) Specifies the Riemann summation method|