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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
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.
boolean_labels: 1-D boolean
Tensor. Entry is
Trueif the corresponding record is in class.
scores: 1-D numeric
Tensor, same shape as boolean_labels.
, 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
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".
Tensor. Fetching this converts internal histograms to auc value.
Op, when run, updates internal histograms.