Computes best sensitivity where specificity is >= specified value.

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.

Usage

metric_sensitivity_at_specificity(
  ...,
  specificity,
  num_thresholds = 200L,
  class_id = NULL,
  name = NULL,
  dtype = NULL
)

Arguments

...

For forward/backward compatability.

specificity

A scalar value in range [0, 1].

num_thresholds

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

class_id

(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.

name

(Optional) string name of the metric instance.

dtype

(Optional) data type of the metric result.

Value

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

Usage

Standalone usage:

m <- metric_sensitivity_at_specificity(specificity = 0.5)
m$update_state(c(0,   0,   0,   1,   1),
               c(0, 0.3, 0.8, 0.3, 0.8))
m$result()

## tf.Tensor(0.5, shape=(), dtype=float32)

m$reset_state()
m$update_state(c(0,   0,   0,   1,   1),
               c(0, 0.3, 0.8, 0.3, 0.8),
               sample_weight = c(1, 1, 2, 2, 1))
m$result()

## tf.Tensor(0.33333334, shape=(), dtype=float32)

Usage with compile() API:

model |> compile(
  optimizer = 'sgd',
  loss = 'binary_crossentropy',
  metrics = list(metric_sensitivity_at_specificity())
)

See also

• https://keras.io/api/metrics/classification_metrics#sensitivityatspecificity-class

Other confusion metrics:
metric_auc()
metric_false_negatives()
metric_false_positives()
metric_precision()
metric_precision_at_recall()
metric_recall()
metric_recall_at_precision()
metric_specificity_at_sensitivity()
metric_true_negatives()
metric_true_positives()

Other metrics:
Metric()
custom_metric()
metric_auc()
metric_binary_accuracy()
metric_binary_crossentropy()
metric_binary_focal_crossentropy()
metric_binary_iou()
metric_categorical_accuracy()
metric_categorical_crossentropy()
metric_categorical_focal_crossentropy()
metric_categorical_hinge()
metric_cosine_similarity()
metric_f1_score()
metric_false_negatives()
metric_false_positives()
metric_fbeta_score()
metric_hinge()
metric_huber()
metric_iou()
metric_kl_divergence()
metric_log_cosh()
metric_log_cosh_error()
metric_mean()
metric_mean_absolute_error()
metric_mean_absolute_percentage_error()
metric_mean_iou()
metric_mean_squared_error()
metric_mean_squared_logarithmic_error()
metric_mean_wrapper()
metric_one_hot_iou()
metric_one_hot_mean_iou()
metric_poisson()
metric_precision()
metric_precision_at_recall()
metric_r2_score()
metric_recall()
metric_recall_at_precision()
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()