metric_sensitivity_at_specificity
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
| Arguments | Description |
|---|---|
| … | 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()