The sensitivity at a given specificity.
metric_sensitivity_at_specificity(
...,
specificity,
num_thresholds = 200L,
class_id = NULL,
name = NULL,
dtype = NULL
)
A (subclassed) Metric
instance that can be passed directly to
compile(metrics = )
, or used as a standalone object. See ?Metric
for
example usage.
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.
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.
Other metrics:
custom_metric()
,
metric_accuracy()
,
metric_auc()
,
metric_binary_accuracy()
,
metric_binary_crossentropy()
,
metric_categorical_accuracy()
,
metric_categorical_crossentropy()
,
metric_categorical_hinge()
,
metric_cosine_similarity()
,
metric_false_negatives()
,
metric_false_positives()
,
metric_hinge()
,
metric_kullback_leibler_divergence()
,
metric_logcosh_error()
,
metric_mean_absolute_error()
,
metric_mean_absolute_percentage_error()
,
metric_mean_iou()
,
metric_mean_relative_error()
,
metric_mean_squared_error()
,
metric_mean_squared_logarithmic_error()
,
metric_mean_tensor()
,
metric_mean_wrapper()
,
metric_mean()
,
metric_poisson()
,
metric_precision_at_recall()
,
metric_precision()
,
metric_recall_at_precision()
,
metric_recall()
,
metric_root_mean_squared_error()
,
metric_sparse_categorical_accuracy()
,
metric_sparse_categorical_crossentropy()
,
metric_sparse_top_k_categorical_accuracy()
,
metric_specificity_at_sensitivity()
,
metric_squared_hinge()
,
metric_sum()
,
metric_top_k_categorical_accuracy()
,
metric_true_negatives()
,
metric_true_positives()