The sensitivity at a given specificity.

  num_thresholds = 200L,
  class_id = NULL,
  name = NULL,
  dtype = NULL



Passed on to the underlying metric. Used for forwards and backwards compatibility.


A scalar value in range [0, 1].


(Optional) Defaults to 200. The number of thresholds to use for matching the given specificity.


(Optional) Integer class ID for which we want binary metrics. This must be in the half-open interval [0, num_classes), where num_classes is the last dimension of predictions.


(Optional) string name of the metric instance.


(Optional) data type of the metric result.


A (subclassed) Metric instance that can be passed directly to compile(metrics = ), or used as a standalone object. See ?Metric for example usage.


Sensitivity measures the proportion of actual positives that are correctly identified as such (tp / (tp + fn)). Specificity measures the proportion of actual negatives that are correctly identified as such (tn / (tn + fp)).

This metric creates four local variables, true_positives, true_negatives, false_positives and false_negatives that are used to compute the sensitivity at the given specificity. The threshold for the given specificity value is computed and used to evaluate the corresponding sensitivity.

If sample_weight is NULL, weights default to 1. Use sample_weight of 0 to mask values.

If class_id is specified, we calculate precision by considering only the entries in the batch for which class_id is above the threshold predictions, and computing the fraction of them for which class_id is indeed a correct label.

For additional information about specificity and sensitivity, see the following.

See also