metric_sensitivity_at_specificity: Computes best sensitivity where specificity is >= specified...

In keras: R Interface to 'Keras'

Computes best sensitivity where specificity is >= specified value

Description

The sensitivity at a given specificity.

Usage

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

Arguments

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

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 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.

Value

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

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_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()

keras documentation built on Aug. 16, 2023, 1:07 a.m.