keras (version 2.8.0)

metric_specificity_at_sensitivity: Computes best specificity where sensitivity is >= specified value

Description

Computes best specificity where sensitivity is >= specified value

Usage

metric_specificity_at_sensitivity(
  ...,
  sensitivity,
  num_thresholds = 200L,
  class_id = NULL,
  name = NULL,
  dtype = NULL
)

Arguments

...

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

sensitivity

A scalar value in range [0, 1].

num_thresholds

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

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

Details

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 specificity at the given sensitivity. The threshold for the given sensitivity value is computed and used to evaluate the corresponding specificity.

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

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_sensitivity_at_specificity(), metric_sparse_categorical_accuracy(), metric_sparse_categorical_crossentropy(), metric_sparse_top_k_categorical_accuracy(), metric_squared_hinge(), metric_sum(), metric_top_k_categorical_accuracy(), metric_true_negatives(), metric_true_positives()